Open New Learning Lab Resources

Most organizations say skills are a priority. But the data suggests something more structural: We’re not just facing a skills gap in the age of Agentic AI. We’re facing a misalignment in what skills actually matter. This visual captures it clearly: Leaders prioritize technological skills while employees emphasize socioemotional skills. And across sectors, that gap is consistent. From our recent work on how organizations can treat skills as a strategic priority, one thing stands out: The challenge isn’t awareness: It’s translation into action! And the implication is more fundamental than it seems: If you over-index on technology without the human layer, you don’t accelerate the AI transformation, you fragment it. At the same time, if you underinvest in socioemotional capabilities, you limit the organization’s ability to absorb and apply change. This is not a trade-off. It’s a design problem. Skills need to be treated as a strategic asset, not a training topic. Which means: linking skills directly to business priorities planning them over multi-year horizons and aligning leadership and workforce on what “future-ready” actually means. Because the organizations that get this right won’t just close gaps. They’ll build the capability to continuously reconfigure themselves. If you want to learn more, see my latest article co-authored with Tania Zulu Holt, Tunde Olanrewaju CBE and Ulf Schrader in the comments. #AI #AgenticAI #FutureOfWork #Innovation #Strategy

🎙️ "HR is no longer an enabling function but a driver of transformation and value creation." 🎬 Presenting the best HR, future of work and people analytics resources for March: https://lnkd.in/eUruif_P 🎬 Selections this month include: 🔎 Boston Consulting Group (BCG)'s Creating People Advantage report finds that leading CHROs are shifting from support roles to driving business performance, but that many HR functions are not yet equipped to do so. The report outlines the biggest gaps where CHROs should focus (see Figure). These include Workforce Planning, People Analytics and Leadership Development. 🔎 Norm Smallwood and Dave Ulrich outline seven steps to deliver Stakeholder HR, where HR creates value not only for employees and leaders but also for customers, investors and communities. 🔎 Amy Edmondson and Dr Tomas Chamorro-Premuzic explain why eliminating entry-level jobs is short-sighted and what leaders should do instead. 🔎 Maria Nolazco Masson, Paola Alfaro Alpízar and Cole Napper reveal the current state of people analytics in LATAM. 🔎 Shonna Waters, PhD and Colby Kennedy Nesbitt, Ph.D. present five design principles for AI in performance management. 🔎 Fresh from his stellar keynote at UNLEASH, Peter Hinssen shares why embracing uncertainty is an absolute necessity and how HR can unlock its most underutilised asset - unstructured data. 🔎 Al Adamsen reframes employee experience as a system shaping culture, capability, and performance. 🎙️ "EX describes the totality of what an employee perceives, feels, and makes meaning of across their relationship with work and the organization." 🔎 Josh Bersin argues that AI is not eliminating jobs but redesigning them into “superjobs.” 🔎 Sanja Licina, Ph.D. and Serena H. Huang, Ph.D. discuss how companies can move beyond good intentions and start building environments where people feel safe to speak up, contribute, and challenge the status quo. 🔎 Dan Riley and Yuyan Sun break down what it means to be productive, what happens when AI moves faster than our ability to understand it, and the cost of optimising speed at the expense of connection. 📚 Book of the Month: Eric Knauf - The 56% Solution: How Belonging Infrastructure Transforms Performance. 🎬 Video of the Month: Brené Brown and Adam Grant discuss what they will never agree on, and what healthy authenticity looks like in practice, not just in principle. 🙏 Thanks to John Gray, Rob Slane and the Lightcast team for sponsoring the March edition of the Data Driven HR Monthly. You can access their brilliant report, which details The Three Fault Lines Reshaping Workforce Strategy. 🫶 I hope you learn as much from this month’s edition as I did from compiling it. If so, please share with your network by commenting or sharing this post and subscribe here: https://lnkd.in/g8Awizjk | 36 comments on LinkedIn

A major event happened in the world of AI today. Anthropic accidentally leaked the future of AI agents. 512,000 lines of Claude Code's source code were accidentally included in a routine software update. Within hours, developers had reverse-engineered the entire thing. The security story is interesting. But the feature roadmap story? That's what every learning professional should be paying attention to. Buried in the code are 44 unreleased features. Fully compiled, production-ready code. Three of them fundamentally change the game for anyone producing learning content at scale: KAIROS is the always-on agent. Named after the Greek concept of "the right time," this is a persistent background mode. It watches your working environment, consolidates its own memory while you're idle, and proactively acts on what it notices. Imagine an agent that monitors an SOP version, notices it's updated, and automatically regenerates your performance support experience without being told. Coordinator Mode is multi-agent orchestration. One Claude instance spawns and manages multiple worker agents running in parallel. Not "I'll run three things one at a time." Actual parallel execution with convergence. This is for anyone managing content pipelines with multiple derivative outputs. Think of a single training video that needs to become an email communication, step-by-step instructions, and a micro lesson. Agent Triggers + Proactive Mode: This is the self-running pipeline. It's like setting a recurring calendar event, except instead of reminding you to do something, it tells the AI agent to do something. Oh, and it can sleep, wake on a condition, and resume. Anthropic accidentally showed its cards, and it looks like it's holding 4 aces (agents?). We all talk about AI-driven content production efficiency, but this is so much more. It's about the fundamental nature of how L&D teams will operate. When AI agents can orchestrate multi-step workflows autonomously, including watching for triggers, running parallel processes, and maintaining a persistent record of where a project stands, we stop thinking in terms of "tools I use" and start thinking in terms of "systems I supervise." And it applies everywhere: course development pipelines, assessment generation, content localization workflows, compliance update cycles, and onboarding content refreshes. This is content-zero, where production time and cost go towards zero. The organizations that figure these new agent capabilities out first will not just move faster. They'll operate at a fundamentally different scale with the same-sized team.

Training is rarely the answer. A meta-analysis (Slemp et al., 2024) of organisational interventions found... → Positive effects? Yes → Meaningful performance change? Inconsistent → Sustainability? Weak Most interventions work in theory...They just don’t survive contact with the system. Three findings worth paying attention to... 1️⃣ “Soft” interventions outperform structural ones Leadership, team dynamics, communication → consistently stronger effects than process or tech changes Not because they’re softer...Because they operate closer to behaviour. 2️⃣ Participation changes everything When people are involved → impact increases When interventions are imposed → impact decays Buy-in isn’t a “nice to have”...It’s a performance variable. 3️⃣ Same intervention, different result Context isn’t A factor...It’s THE factor. Culture, leadership, environment → determine whether anything sticks. Here’s the mistake most organisations make... They treat interventions as the lever. They’re not...The system is the lever. You can roll out: training new processes engagement initiatives But if the system: doesn’t reinforce the behaviour doesn’t create the conditions or actively works against it Nothing changes. Performance isn’t installed...It emerges. And it emerges from the interaction between: environment leadership expectations reinforcement Not from a workshop. Are you designing interventions…Or designing the conditions for behaviour to occur?

I. Situation  1. AI is poised to reshape work but lacks a predictive framework for where it is truly viable 2. A deep ontology of ~40,000 activities enables systematic mapping of AI across tasks 3. The strategic challenge is shifting from fragmented use cases to activity-level deployment logic II. AI Usage Concentration 1. AI adoption is highly skewed, with the top 1.6% of activities capturing over 60% of market value 2. 92% of applications cluster into just 6.8% of activities, revealing extreme concentration 3. This signals both saturation in core domains and outsized opportunity in underpenetrated tasks III. Dominance of Information Work 1. 72% of AI value sits in information-based activities, led by creation (36%) 2. “Think” activities dominate (~92%), especially creating, transferring, and modifying information 3. Physical work remains underpenetrated, positioning robotics as the primary expansion frontier IV. Core Activity Clusters 1. High-frequency use cases concentrate in generative creation (text, image, video, code) 2. Information transfer and summarization act as scaling layers across workflows. 3. Information modification (editing, converting, optimizing) operationalizes and refines outputs V. Interaction vs. Creation Dynamics 1. “Interact” (~32%) is largely driven by information transfer rather than standalone interaction 2. Most interactive systems are structurally extensions of information-processing workflows 3. AI advantage accrues to systems that control and transform information flows, not interfaces VI. Conclusion  1. AI impact is concentrated, not universal, with dominance in information-centric work 2. The ontology exposes both saturated domains and high-value whitespace 3. Competitive advantage will come from targeting underpenetrated activities and recombining workflows into AI-native systems Make sure to check out the insightful article by Alice Cai, Iman Yeckehzaare, Shuo SUN, Vasiliki Charisi, Xinru Wang, Aiman Imran, Robert Laubacher, Alok Prakash, Thomas Malone here: https://lnkd.in/eTQgjrAy _______ 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. #ai #ontology #robotics #futureofwork #transfomativeinnovation #futures #foresight #systems #systemschange #strategy

Microsoft just eliminated its entire senior HR leadership in a single announcement. 70 years of HR leadership. Gone in one internal memo. What organisational compression means for you: your function could be next. Four executives. Kristen Roby Dimlow: 28 years in talent acquisition. Chuck Edward: 22 years of HR business partnerships. Dawn Klinghoffer: 20+ years building people analytics. All departing simultaneously. The company called it an "AI-powered transformation." This is not a headline about diversity policy. It is a headline about workforce architecture. The function is being rebuilt around four new pillars: skills intelligence, AI-enabled workforce planning, product-aligned people support, and culture. Microsoft's chief people officer wrote that the goal is to organise around "the capabilities that will matter most next." That sentence deserves a second read. Because it does not describe the roles being eliminated. It describes the roles replacing them. The new structure does not replicate the old one with fewer people. It is built around fundamentally different assumptions about what HR can do when AI handles the analytical and administrative layer. This is the pattern I have been tracking across Big Tech for months. Meta moved to 50:1 manager-to-employee ratios. Google cut 10% of its management layers. Apple quietly restructured its AI division. Now Microsoft is not just compressing headcount. It is redesigning the logic of how a function operates. I watched a version of this happen firsthand when I ran Meta's operations in Denmark. We went from eight organisational layers to four in eighteen months. The part that never made the headlines: the people who stayed did not keep their old jobs with AI bolted on. They got entirely new jobs. Coordination roles became amplification roles. The skillset changed. The reporting lines changed. The definition of what "good" looked like changed. That is the difference between cost-cutting and structural redesign. Cost-cutting removes people. Structural redesign removes the need for the old architecture entirely. Most companies are still doing the first. Microsoft is doing the second. Here is what concerns me. The gap between what AI enables and what most organisations have actually restructured around continues to widen. Microsoft, Meta, Google, and Amazon are on one side of that gap. Almost everyone else is on the other. And the distance between the two groups is growing every quarter. Your CHRO is watching this. Your board should be too. The question worth raising in your next leadership meeting is not "how do we make our HR team more efficient with AI." It is: if we rebuilt this function from scratch around AI-era capabilities, what would it look like? Not which roles become faster. Which roles would not exist at all. And what replaces them. Most organisations are not asking that question yet. Microsoft just did. | 238 comments on LinkedIn

There's a paper in Nature that deserves more attention than it got. The finding: if you train AI models on data generated by previous AI models, the models progressively forget the edges of reality. Not dramatically but quietly. Rare knowledge disappears first. Outputs converge toward the average. Each generation becomes a slightly blurrier photocopy of the one before. They call it model collapse, and it's not hypothetical, they demonstrated it across LLMs, image generators, and other architectures. The implication that nobody is talking about loudly enough: as AI-generated content floods the internet, the data future models train on becomes increasingly synthetic. Which means the thing that made these models powerful in the first place, exposure to the full messy diversity of human thought and expression, slowly erodes. The researchers make a pointed observation at the end: genuine human interactions with AI systems will become increasingly valuable training data precisely because of this dynamic. The most important raw material for the next generation of AI is something that gets harder to find the more AI succeeds. | 156 comments on LinkedIn

I’ve been rereading one of my favourite books, Never Let Me Go, a 2005 dystopian novel by Kazuo Ishiguro. It starts as a story of children at an idylic English boarding school, full of friendship, creativity, and small obsessions. Gradually the truth emerges: these children are clones, created to provide organs for others. Their art, their self-expression (all supposedly proof of soul) exist as evidence for a system that will never allow them to flourish or extend their lives. This, I think, resonates with how we encounter generative AI. Both situations confront a mismatch between signal and life, between evidence and moral response. In Ishiguro’s novel, the clones’ art is never considered as sufficient evidence to support their requests to extend their lives. With Generative AI, our reading of outputs, untethered from life and accountability, may nonetheless misattribute agency, while obscuring the moral work of attending to real human minds whose life and cognition do depend on relational recognition. In other words, the ethical and existential question is “what do we owe generative AI?” Generative AI, like Ishiguro’s clones, shows us how we measure and relate, or fail to, when life and mind is truly at stake.

Manchmal gibt es Organisationen, die machen bei der AI-Adoption vieles richtig.Die Berliner Volksbank gehört dazu.Im Podcast habe ich mit Niclas Breier gespr...

Honestly, this moment from my conversation with Dr Philippa Hardman changed how I think about what we're building. She talked about how the real shift with AI isn't just making content faster. It's that for the first time, organizations can treat learning as a living system, not a collection of static assets you build once and forget. Because that's exactly the problem we kept hearing from training teams. They'd spend months building a course, and by the time it launched, half of it was already outdated. The issue was never "we need more content." It was "we need content that keeps up." That insight shaped so much of what became Colossyan Learn. We built it so teams can turn knowledge into structured, video-led training and actually maintain it over time. Update it, version it, scale it as the organization changes. Philippa put into words what we've been building toward for the past year. Grateful for this conversation, and excited about what comes next. Full clip below. Link to try Colossyan Learn in the comments. I'm curious: for teams out there, how much of your current training content is already outdated?

How organizations can overcome the seven sources of friction that stall progress.

Most teams never become high-performing. Not because they lack talent. Not because they lack effort. But because they lack the conditions that make performance possible. Research suggests it takes 6–8 months for a team to become high-performing, and only ~15% ever reach that level. The reason is simple... We often focus on who is in the team, when we should be focusing on how the team works together. A recent study examining the development of high-performing teams identified 10 qualities that consistently show up in teams that outperform others: • Clear shared goals • Complementary expertise • Aligned outward action • Supportive communication • Clear rules of collaboration • Respect for differences • Openness to ideas and emotions • Constructive conflict handling • Mutual trust • Deep collaboration But here’s the interesting part...High-performing teams don’t emerge by accident. They develop through structured reflection, facilitated dialogue, and deliberate team-building interventions. In the study, teams that worked through a facilitated development process improved in three critical areas: 1️⃣ Goal clarity and role definition 2️⃣ Collaboration and communication 3️⃣ Team climate and trust In other words: High-performance wasn’t about working harder...It was about working better together. This aligns with a pattern I see repeatedly in organisations. Performance isn’t just a function of individual capability...It’s a function of how well the system enables people to work together. Because even brilliant individuals struggle in poorly designed team environments. But average individuals can achieve extraordinary results in the right conditions. And that’s the real leadership challenge. Not building better individuals...Building better systems for teams to perform within.

The shift from "AI as content machine" to "AI as practice engine" is a shift that only the most advanced L&D teams have made -- here's how to do it. Most L&D folks are still using AI to create content. But what if instead of asking "how can I work with AI to transmit information?" we ask, "how can I work with AI to develop my learners' skills & behaviours?". In my latest blog post, I break down four ways to turn AI into a practice engine — each with a copy‑and‑paste prompt you can drop into Claude, ChatGPT, or Gemini to get a working, ready to test prototype in minutes... _______________ → Structured Roleplay — learners practise high-stakes conversations with an AI character who pushes back. They then get immediate, actionable feedback on how they handled it, which builds competence and confidence (Ericsson, 1993; Bele et al., 2023). → Decision Simulator — learners make a sequence of decisions with cumulative, realistic consequences then reflect on their reasoning. This approach builds the kind of transfer that case studies alone can’t (van Merriënboer & Kirschner, 2017). → Feedback Simulator — learners submit a real piece of work and get situated, criteria-based feedback from an AI reviewer with explicit priorities. This strengthens task-specific competency through active reflection and iteration (Nicol & Macfarlane-Dick, 2006; Tai et al., 2018). → Adaptive Case Study — learners interview a biased, challenging character, e.g. to diagnose a problem. This develops inquiry and problem-finding skills by forcing learners to ask better questions and defend their position (Jonassen, 2000).​​​​​​​​​​​​​​​​ _______________ 🥡 The key takeaway: AI isn’t pedagogically smart by default — but with your input it can become an engine that makes “learning by doing” cheap, repeatable, and scalable in ways we couldn’t deliver before. Read the full breakdown and try examples of each of these for yourself (link in comments) 👇 Happy innovating! Phil 👋 | 20 comments on LinkedIn

L&D does not exist in a vacuum, and AI is changing how learning happens, what does this mean for L&D?

Most organisations say they want a “learning culture.” But almost none understand how workplace learning actually happens. A meta-synthesis (Deutscher & Braunstein, 2023) analysed 94 workplace learning surveys containing 3,688 measurement items. The result? Workplace learning consistently falls into three conditions... 1️⃣ Environment (Presage) The conditions people work inside. • Working conditions • Organisational structure • Learning culture 2️⃣ Interaction (Process) What happens during the work itself. • Task design • Social learning with colleagues • Coaching, support and feedback 3️⃣ Outcomes (Product) What actually changes. • Professional capability • Occupational identity • Career development But here’s the uncomfortable finding...Most research measures the environment and the interactions...Very little measures the actual learning outcomes. In other words: We obsess over learning activities… …but rarely measure whether learning happened. For organisational leaders, this matters. Because workplace learning is not primarily driven by training programmes. It emerges from the system people work inside. Task design. Feedback loops. Social inclusion. Autonomy. Psychological safety. These are the true drivers of capability development...Which is why I keep coming back to this idea: Performance is a system property. ❌ Not a training problem. ❌ Not a motivation problem. ❌ And usually not a people problem. If you want a learning organisation…design work that makes learning unavoidable.

Today in things I think about and ask myself:

The rapid adoption of Large Language Models (LLMs) such as GPT-4 and DeepSeek R1 is transforming learning in higher education, yet unstructured use ca…

Research suggests 70–90% of workplace capability is developed informally while people are doing the work, not in courses, workshops, or e-learning. Yet most organisations still design their development strategy around formal training events....That’s a mismatch. A 2022 review by Tannenbaum & Wolfson describes something called Informal Field-Based Learning (IFBL), the everyday learning that happens through: 💠 Asking questions 💠 Observing experts 💠 Experimenting with new approaches 💠 Reflecting on experience But this learning doesn’t just “happen”...It depends on readiness conditions. Their research highlights five factors that determine whether informal learning actually occurs: 1️⃣ Capability – do people know how to learn from work? 2️⃣ Awareness – do they recognise learning opportunities? 3️⃣ Motivation – are they willing to seek feedback and experiment? 4️⃣ Opportunity – does the work environment create learning moments? 5️⃣ Support – do leaders and peers encourage learning behaviours? In other words...Learning is a system outcome, not an individual trait. (ooh where have we heard this before) If the environment lacks time, psychological safety, or access to expertise, learning collapses, regardless of how many training courses you run. This has a major implication for organisational performance... 👉 Development is less about delivering training 👉 and more about designing learning-rich work environments Because ultimately...Capability isn’t built in classrooms...It’s built in the field. | 13 comments on LinkedIn

𝘛𝘳𝘢𝘪𝘯𝘪𝘯𝘨 𝘥𝘰𝘦𝘴𝘯'𝘵 𝘥𝘳𝘪𝘷𝘦 𝘭𝘦𝘢𝘳𝘯𝘪𝘯𝘨 𝘢𝘵 𝘸𝘰𝘳𝘬--𝘪𝘯𝘥𝘪𝘷𝘪𝘥𝘶𝘢𝘭 𝘮𝘰𝘵𝘪𝘷𝘢𝘵𝘪𝘰𝘯 𝘢𝘯𝘥 𝘴𝘺𝘴𝘵𝘦𝘮𝘴 𝘥𝘰. This is so true and so painfully obvious, I seriously don't understand why anyone would think otherwise, including and especially business leaders. Okay, fine, I kind of do. 1️⃣ Society talks about learning like it's a radio signal. Experts "transmit" their knowledge and "deliver" information, while learners act as receptors that "soak it up" so their minds are "filled" with new information. (GAG.) As a result, most people have a deeply inaccurate mental model of how learning works. 2️⃣ L&D has done a great job reinforcing this inaccuracy. The business needs employees to work in new ways and asks for training, bc 1️⃣. Instead of meekly delivering the training and hoping for the best, we should be saying "yes, and": yes, we can create an awesome training for you, AND it will not move the needle unless you, business, change the incentives/systems to support the new way of working. Our silence on the individual and organizational factors that drive employees to WANT to learn and behave differently just tells the business that training is the solution. 3️⃣ When the business asks for metrics showing L&D impact, we report stuff that continues to reinforce the inaccurate mental model of how learning works. Open rates, courses in the catalog, satisfaction scores, percent of employees trained--all of these metrics tacitly tell the business that employees are little receptors that have soaked up whatever we transmitted to them. L&D is deeply complicit its own second-class citizenry. We complain all the time that the business doesn't "get" L&D and that we deserve a seat at the table, yet we actively fail to advocate for the ecosystem needed for learning, AND we knowingly waste resources on trainings that won't work in isolation. The rapid shift toward AI will bring this situation to a head. L&D leaders of tomorrow will be the ones who break the business of its train-and-magic-will-happen thinking and partner with them to create an operating model that drives actual learning and behavior change. #learninganddevelopment #HR #organizationaldevelopment #learningscience | 17 comments on LinkedIn

Die neue App „Einstein“ war erst Stunden online… und die Hochschulwelt bereits in Aufruhr.

So much Gamification advice comes from people who don't understand human psychology.

𝗕𝗮𝗻𝗸𝗲𝗻 𝗲𝗿𝗻𝘁𝗲𝗻 𝗱𝗶𝗲 𝗙𝗿𝘂̈𝗰𝗵𝘁𝗲 𝗶𝗵𝗿𝗲𝗿 𝗞𝗜-𝗜𝗻𝘃𝗲𝘀𝘁𝗶𝘁𝗶𝗼𝗻𝗲𝗻

The Anthropic Economic Index reveals the shape of AI adoption across the world. Here, you can explore the data behind our research to understand how people are using Claude across every US state and hundreds of occupations. Track the topics that are trending where you live, and see how people are using AI to augment or automate their work—that is, whether they prefer to collaborate with, or delegate to, Claude.

Künstliche Intelligenz ist in vielen Unternehmen im Arbeitsalltag angekommen – doch ihre Wirkung auf Lernen und Leistung ist nicht eindeutig. In der ZP Nachg...

𝗗𝗲𝘂𝘁𝘀𝗰𝗵𝗹𝗮𝗻𝗱 𝘀𝗰𝗵𝗮𝗳𝗳𝘁 𝗙𝗮𝗸𝘁𝗲𝗻 𝗶𝗻 𝗱𝗲𝗿 𝗞𝗜-𝗥𝗲𝗴𝘂𝗹𝗶𝗲𝗿𝘂𝗻𝗴 ...

The themes in summary: ⭐ Unlocking the AI-enabled organization Although 88% of organizations are experimenting with AI, 81% report no meaningful bottom-line gains. To capture real value, leaders must push for a double transformation that reimagines workflows across the entire enterprise rather than relying on fragmented use cases. Executives should prioritize building flexible technology platforms and an “AI mesh” to scale agentic systems securely. They must empower their workforce by actively managing job replacement fears and embedding clear ethical guidelines to foster deep organizational trust. ⭐ Humans and AI agents: Building a new world of collaboration As workplaces transition to an AI-hybrid model, roughly 75% of current roles will require reshaping to blend technological fluency with higher cognitive skills. Despite this massive shift, only 25% of leaders expect AI to act as autonomous teammates in the short term, with most still viewing it merely as a support tool. To unlock the exponential productivity gains anticipated by 55% of executives, organizations must redesign workflows from the ground up around human-agent collaboration. Leaders need to establish robust sensing mechanisms to track adoption progress and establish a culture of continuous learning and experimentation. ⭐ Leveraging AI to rewrite the future of shared services Traditional shared-services centers are rapidly evolving, with 84% of organizations planning to expand their operational scope over the next two years. However, just 6% of business service leaders are currently realizing the full benefits of advanced technologies across multiple use cases. Companies must pivot toward AI-native global business services models that are based on orchestration of end-to-end automation, rather than relocation.

DIE GUT SITUIERTE MITTELSCHICHT - BESITZT KAUM ERFAHRUNG MIT STRUKTURELLEM ARBEITSMARKTDRUCK - frühere Automatisierungswellen betrafen andere.

Niemand scheitert gerne. Doch manchmal führt genau das zum Erfolg: Hätte Alexander Fleming ordentlich aufgeräumt, gäbe es kein Penicillin. Forschende wissen ...