Open New Learning Lab Resources

AI adoption is slow because leaders can't mandate it in the same way they mandate Excel for their accountants. Because AI doesn't replace a tool - it replaces a way of thinking. Which means adoption is much closer to merging with a new company than rolling out a new software...which is why enterprise wide adoption has to live under Talent/HR. Consider what you do when you roll out a new CRM system. You train everyone up, you'll have your complainers that prefer the old system, then you finally sunset the former system and bam, everyone is now using the new CRM. That's mandating a tool. If you want to work at this company, you have to use this tool. That's also why we refer to getting good with technology as getting "proficient." But notice that Anthropic and others have stopped using the word proficient. They now use the word "behavior." All this is to say: If you're a leader trying to drive transformation, you can't treat it like something you can just teach with use cases, features, prompting. It won't work - we've all seen that. We have to get people thinking differently with it, working with it like the new set of teammates that have just joined from that digital alien company. That's why HR and Talent have to lead the way (outside product development). They understand culture, learning, and change. | 55 comments on LinkedIn

The number that should terrify every L&D leader isn't the budget cut. It's this one. In 1996, roughly 75% of what employees needed to perform their jobs came from formal training. Today, that number has collapsed to somewhere between 8 and 10%. Let that sink in. We've built entire L&D functions, learning management systems, and instructional design methodologies around a world that no longer exists. The work has changed. The speed of change has changed. The nature of knowledge has changed. And yet, many organizations are still pouring resources into the same event-based training model the one built for a slower, more stable era. The question isn't whether your training is good. It's whether training alone can ever be enough. If 90% of what people need to perform lives outside the formal learning event, then our job has fundamentally shifted. We're not curriculum architects anymore. We're performance ecosystem designers. What are you doing today to close the gap between what formal training can deliver and what your people actually need to perform? | 35 comments on LinkedIn

"𝘞𝘦'𝘳𝘦 𝘵𝘳𝘢𝘯𝘴𝘧𝘰𝘳𝘮𝘪𝘯𝘨 𝘸𝘪𝘵𝘩 𝘈𝘐." Every deck says it. Every all-hands repeats it. Then the same people get promoted for the same things they were promoted for ten years ago. This is what I call 𝗩𝗮𝗹𝘂𝗲–𝗥𝗲𝘄𝗮𝗿𝗱 𝗗𝗶𝘀𝘀𝗼𝗻𝗮𝗻𝗰𝗲. The behaviours that create value have changed. The behaviours that get rewarded have not. AI is changing where value is created. As execution becomes easier, value shifts towards judgment, creativity, integration, orchestration and decision-making. Yet many organisations still reward:   • Volume over value   • Answers over questions   • Presence over impact   • Speed over judgment   • Activity over outcomes Then we wonder why behaviour does not change. Here is the part most transformations miss. Using AI is not the same as creating value with AI. The value comes from people who challenge outputs, connect expertise, integrate perspectives, orchestrate work and make the call. 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗿𝘂𝗻𝘀 𝗱𝗲𝗲𝗽𝗲𝗿 𝘁𝗵𝗮𝗻 𝗳𝗼𝗿𝗺𝗮𝗹 𝗶𝗻𝗰𝗲𝗻𝘁𝗶𝘃𝗲𝘀. 𝗣𝗲𝗼𝗽𝗹𝗲 𝗽𝗮𝘆 𝗳𝗮𝗿 𝗹𝗲𝘀𝘀 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝘁𝗼 𝘄𝗵𝗮𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗮𝘆 𝘁𝗵𝗮𝗻 𝘁𝗼 𝘄𝗵𝗼 𝗴𝗲𝘁𝘀 𝗮𝗵𝗲𝗮𝗱. If an organisation claims to value curiosity, collaboration, integration and human–AI orchestration, then the people being promoted need to embody those behaviours. 𝗕𝗲𝗰𝗮𝘂𝘀𝗲 𝗰𝘂𝗹𝘁𝘂𝗿𝗲 𝗶𝘀 𝗻𝗼𝘁 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗯𝘆 𝘃𝗮𝗹𝘂𝗲𝘀 𝗼𝗻 𝗮 𝘀𝗹𝗶𝗱𝗲. 𝗜𝘁 𝗶𝘀 𝗱𝗲𝗳𝗶𝗻𝗲𝗱 𝗯𝘆 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲 𝘄𝗵𝗼 𝘀𝘂𝗰𝗰𝗲𝗲𝗱. If the people getting ahead are still being rewarded for certainty, visibility and proximity to power, everyone gets the message. No matter what the strategy says. Organisations get the behaviours they reward. Cultures become what they celebrate. #humanai #workdesign #aivaluecreation

If you want to understand why AI hasn't completely transformed your organization's outputs overnight, you need to understand the "weak link" problem. Professor Chad Jones recently gave a phenomenal talk at Stanford titled "A.I. and Our Economic Future," where he highlighted how explosive economic growth is currently bottlenecked by the tasks that still require human intervention. (Based upon risk, many tasks should remain human.) A business workflow is like a chain: If you make 17 of 20 links incredibly strong with AI agents and automation, the chain's overall strength is still capped by the three weakest links. Examples of weak links include various forms of human work (review), real-world machine completion, the supply chain, and even AI overstepping its capabilities. When AI successfully automates a large portion of a role (workflows), the remaining human tasks become the scarce, highly valued weak links. Which is why I believe salaries for humans running policy on automations will rise significantly. Now, if you break just one small link (task failure), it can seriously limit or destroy an entire system. It could be catastrophic. This reality is exactly what Conrad Gottfredson and I address in our Matrix framework. The Gottfredson-Cavalier Matrix evaluates tasks across two dimensions that perfectly mirror these weak link economics: - Level of Difficulty: The vertical axis captures the upside bottleneck. The hardest tasks are the weak links that cap a role's productivity gains until they, too, can be reliably automated by a human-running policy. - Critical Impact of Failure: The horizontal axis measures the downside risk. The severity of a broken link does not scale with its difficulty; a cheap, routine task can be the exact one whose failure causes a catastrophic event. We then determine the amount of work a human and an AI contribute to task completion. This is the Human-AI Task Scale (Values 1-7). The task is now positioned on the Matrix. The future of work is not about eliminating roles, but reshaping them around human discernment and high-stakes tasks. The organizations that win the next decade will be the ones that can see the human-machine relationship clearly, measure it precisely, and govern it deliberately. Which is what we are enabling at Talent Rewire. The video link is in the comments, and the Matrix link is there as well, where you can explore the details.

"Ich brauche keine KI Architektur - xyzGPT reicht mir." Das war eine Wette darauf, dass die Preise bei Anthropic und OpenAI niedrig und politische Machtspielchen ausbleiben.

It's true: using AI can erode our cognition. But only if we let it. We can choose how we interact with AI so that it enhances our cognition, potentially to the point that we think better even after the AI is taken away. I've redone my one-page 8 levels framework as a series of compact slides for each level, as this itself helps cognition and how we absorb information. As I continually emphasize in my keynotes, executive sessions, and leadership programs, the master capability is metacognition. The necessary first step is to recognize at which level we are working. That is the unlock to change how we are using the tools so we move up the spectrum to more enabling forms of interaction. There are an array of valuable skills, techniques, and approaches that assist us at each of the higher levels. But simply beginning to be aware of how your thinking is impacted by your AI use allows you to change and improve. | 20 comments on LinkedIn

Siara KI von eEight: implizites Wissen sichern, Performance Support aufbauen, Dashboards fürs Management. Wissen rein, Performance raus.In diesem Video zeige...

Eine Autorengruppe verschiedener Hochschulen (Australien, Neuseeland, USA, China) legt ein Rahmenmodell für «AI Learning Companions» vor: KI-Begleiter, die nicht auf schnelle Aufgabenerledigung, sondern auf nachhaltiges Lernen hin entwickelt werden. Der Pre-Print-Beitrag begründet, warum LLMs in ihr

Explore how HR leaders navigate their dual mandate by guiding enterprise AI transformation while simultaneously reimagining the HR function itself.

DIE WISSENSGESELLSCHAFT AUTOMATISIERT GERADE IHRE EIGENEN LEHRJAHRE.

🎙️"The real question isn’t whether people have the right skills. It’s whether the organization is built to unlock them" Microsoft’s 2026 Work Trend Index argues that the next competitive advantage will belong to what it calls Frontier Firms. These are organisations that move beyond AI adoption to AI absorption, redesigning work around people, agents and continuous learning. The report is structured around three powerful ideas. 1️⃣ AI lifts the ceiling on individual potential. Analysis of more than 100,000 Copilot interactions found that almost half of AI use supports higher-order cognitive work such as analysing information, solving problems and making decisions. Two-thirds of users say AI enables them to spend more time on higher-value work, while 58% report producing work they could not have done a year ago. The most advanced users - 'Frontier Professionals' - are not simply working faster. They are redesigning workflows, orchestrating agents and applying stronger judgment, critical thinking and quality control. 2️⃣ The job of every leader is to rearchitect work. This is arguably the report’s most important insight: the Transformation Paradox (see page 12). Only 19% of workers sit in the Frontier zone where individual AI capability and organisational readiness reinforce each other. Half remain in an emergent middle ground, while others are either stalled, blocked by organisational barriers, or working in companies that are ready but whose employees have yet to catch up. The challenge is not technology. It is systems design. Employees are often ready to reinvent work, but incentives, metrics and management practices continue to reward the old way of working. 3️⃣ Every firm is a Learning System. Microsoft finds that organisational factors such as culture, manager support and talent practices account for roughly twice the impact of individual effort alone. The organisations pulling ahead are those that systematically capture what employees learn, codify successful human-agent workflows and scale those practices across the enterprise. 🎬 The key takeaway Frontier Firms redesign roles and workflows, align leadership and incentives, build manager capability, define effective human-agent handoffs and establish governance that keeps human judgment at the centre. In an era where intelligence is increasingly available on demand, the organisations that learn fastest, not merely deploy fastest, will be best positioned to lead. 🔗 The study is featured in the May edition of the Data Driven HR Monthly, which you can access here: https://lnkd.in/enxVdRH9 🔗 | 16 comments on LinkedIn

𝗞𝗜-𝗪𝗲𝗻𝗱𝗲 𝗮𝗺 𝗝𝗼𝗯𝗺𝗮𝗿𝗸𝘁: 𝗔𝗿𝗯𝗲𝗶𝘁 𝘄𝗮𝗻𝗱𝗲𝗿𝘁 𝘇𝘂 𝗞𝗜-𝗳𝗲𝗿𝗻𝗲𝗻 𝗧𝗮̈𝘁𝗶𝗴𝗸𝗲𝗶𝘁𝗲𝗻

Learning must be at the heart of all work. A re-analysis of human-AI collaboration research shows that if you actively enable human learning in the collaboration process, you achieve considerably better outcomes. I am continually frustrated by the research on human-AI collaboration, because much of it focuses on narrow reductionist cases that are massively over-generalized. This is exemplified by a recent meta-analysis that purported to show that human-AI synergy - as in outperforming either alone - was highly limited. However reviewing the underlying research to uncover which of the studies incorporated a learning loop so the human could learn and improve in the process (relatively few) showed that this is a key driver of synergy. I have long said there are two ways for humans and AI to work together: ➡️ ones where the task is bounded and the human is unchanged, which essentially means their capabilities erode over time ➡️ ones where the human learns and improves Every human-AI interaction must enable human learning. Building this into research - and of course all real-world work - will support superior Humans + AI value creation. Thank you for this excellent research and insight Julian Berger Jason Burton Thomas Kosch Ralf Kurvers Christopher Lazik Linda Onnasch Tobias Rieger Anna Thoma Dirk Wulff Stefan Herzog et al

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The future of Learning and Development (L&D) isn't a bigger classroom. It's no classroom at all. I just read through RedThread Research's 2026 State of L&D report, and it crystallized something I've been saying for years: the organizations winning at development are the ones that stopped treating learning as a place you go and started building it into the work itself. What stood out most in this report? The single most relied-upon way people develop is manager feedback, not courses, not platforms. In fact, none of the methods L&D traditionally owns cracked the top ten this year. That's not an indictment. It's an invitation to build better and build something new. The biggest lever we have is enabling managers to coach, and most of us are underinvesting in it. I The best functions today have collapsed the space between L&D and where work actually happens. The shift from "learning alongside work" to "learning inside work" has been talked about for a decade. The 2026 data shows it's finally happening. While I am thrilled to have this research and very grateful to be a part of the RedThread community, the findings are not shocking. What is shocking is how few organizations seem to get it. If you care about this stuff, then you definitely should consider joining the RedThread Research community | 12 comments on LinkedIn

NVIDIA CEO Huang hat die ehrlichste KI-Rede aller Zeiten gehalten & die Tokenisierung der Welt beschrieben: „The more you buy, the more you make!" - Teleshopping im Jahr 2026! 👇 Dass ich jemals einen Rant ueber eine NVIDIA Keynote veroeffentlichen werde, das haette ich auch nicht gedacht, aber in meiner aktuellen Podcastausgabe 👉 https://t.ly/NVIDIA war es dann so weit! Huang hat die Wertschoepfungskette der KI-Industrie auf eine einzige Metrik reduziert: Compute erzeugt Token, Token sind Umsatz, fertig. Was mit den Token passiert, ob jemand damit etwas Sinnvolles baut oder nur teuren Leerlauf produziert, faellt komplett raus. KI-Gigafabs produzieren Token wie eine Druckerei Papier, und am anderen Ende sitzt der Endkunde, der diese Token verbrennt, um Agents laufen zu lassen – und der „Erfolg" misst sich nicht am Ergebnis, sondern am Verbrauch. Das ist keine Technologiegeschichte mehr, das ist ein Geschaeftsmodell, das sich selbst als Naturgesetz verkauft. Compute ist Umsatz, Compute ist Profit, jeder Token ist Profit. Das Perfide daran ist die Verkuerzung – der Wert entsteht angeblich beim Rechnen selbst, nicht bei dem, was dabei rauskommt. Fuer Nvidia und die Hyperscaler stimmt das sogar, die verdienen an jedem erzeugten Token. Fuer euch, mich und alle anderen Endkunden ist das schlicht eine Rechnung, die nie aufgeht. Huang findet es „zutiefst alarmierend", wenn ein gut bezahlter Entwickler nicht mindestens eine Viertelmillion Dollar an Token-Kosten verursacht. Das ist keine Produktivitaetslogik, das ist Verbrauchszwang als Management-Doktrin. Bei einigen Firmen sehen wir die Folgen schon: Mitarbeiter feuern sinnlose, teure Prompts ab, nur um im Token-Ranking nicht abzustuetzen. Wenn der Verbrauch zur Kennzahl wird, optimiert man auf Verschwendung – und genau das ist gewollt! NVIDIAs Behauptung, KI verdreifache die Produktivitaet, das ist methodisch einfach laecherlich. Es betrachtet ausschliesslich Softwareentwickler, also genau die Gruppe mit dem groessten KI-Nutzen, und blendet die unzaehligen Berufe aus, in denen KI praktisch nichts bringt. Daraus dann abzuleiten, es wuerden mehr statt weniger Jobs entstehen, ist Marketing, keine Analyse. Die eigentliche Pointe: Der KI-Wahn in den Rechenzentren saugt den Speicher ab, treibt die Preise hoch und ruiniert gerade den klassischen PC-Markt – mitverursacht von NVIDIA. Und dann kommt ausgerechnet NVIDIA und erklaert den alten PC fuer tot, um euch mit RTX Spark einen ARM-Notebook-Chip zu verkaufen, auf dem ihr „eure eigenen Token" generieren duerft. Man zerschlaegt erst den Markt und verkauft euch dann die Loesung fuer das selbst geschaffene Problem. Ich habe langsam aber sicher das Gefuehl, dass ich in eine Parallel-Dimension gebeamt wurde, denn all dies findet tatsaechlich gerade statt. Maschinen produzieren fuer Maschinen und wer mehr Maschinen kauft, der kann fuer noch mehr Maschinen produzieren. Das ist kein Kartenhaus, das ist kein Ponzi-Scheme... das ist einfach nur noch Wahnsinn!

Triple Loop Learning & Learning Organizations AI is forcing organizations to relearn how to learn. Many of the ideas from learning organizations and systems thinking are resurfacing today, not because they are trendy again, but because they are suddenly necessary. Why? Because AI adoption is not like implementing a new CRM or ERP system. It requires us to rethink how work gets done, how decisions get made, how teams collaborate, and even how organizations are designed. Which brings me back to one of my favorite models: Triple-Loop Learning. 🔹 Loop #1: Doing Are we doing things right? At this level, we focus on actions and results. We look at outcomes and ask: "What should we do more of, less of, or differently?" Example: "Our AI pilot did not improve productivity, so let's improve the prompts and workflows." This is about improving behavior and execution. 🔹 Loop #2: Thinking Are we doing the right things? At this level, we challenge the assumptions behind our actions. We ask: "What assumptions led us to choose this approach?" Example: "We assumed AI would save time immediately. What if the real value is not efficiency but better decision quality?" This is about improving our thinking. 🔹 Loop #3: Being How do we decide what is right? The original model refers to context. In my work, I often connect this level to the North Star, the deeper purpose, values, and principles that guide our assumptions and decisions. We ask: "What kind of organization are we trying to become?" Example: "Are we adopting AI simply to reduce costs, or are we using it to create more value for customers and more meaningful work for employees?" This is about identity, purpose, and wisdom. -- Many organizations are still operating in Loop #1. The most successful organizations are learning in Loop #2. But the organizations that will thrive in the age of AI will likely master Loop #3. Because AI is not just changing what we do. It is challenging how we think. And ultimately, who we choose to become. Image Credit: Jeroen Kraaijenbrink #Leadership #Strategy #AI #LearningOrganization #SystemsThinking #DigitalTransformation #NorthStarClarity #FutureOfWork | 17 comments on LinkedIn

Two very different AI strategies are playing out right now. Oracle. Meta. Dozens of others. Cutting tens of thousands of people to fund AI investments. They are betting that the technology replaces the humans. Then there's IKEA. They deployed an AI chatbot that now handles 47% of all customer conversations. And instead of using that as a reason to cut staff, they looked at the 53% the bot couldn't handle. Those conversations weren't transactional. Customers wanted design advice. Help with their homes. Guidance on spaces. So IKEA reskilled their customer service staff into interior design consultants. Same people. Just a slightly different job description. That decision generated €1.3 billion in new revenue in the first year. One group of companies asked: how do we cut costs with AI? IKEA asked: what can our people do now that AI handles the routine stuff? Same turbulent technology market. Completely different outcomes. AI doesn't decide what happens to your people. You do. | 300 comments on LinkedIn

This chapter analyzes the economic footprint  of AI across the private sector and its implications for labor markets, productivity, and the future of work.

DOSSIER HOME | BESTELLUNG | KONTAKT | IMPRESSUM Das Kuratierte Dossier Bd.8„Knowledge and Co-Creation“ Print per eMail bestellen Das Kuratierte Dossier Band 8 im Mai 2026 zum Schwerpunkt beim KnowledgeCamp #gkc25 im Oktober 2025 in Berlin Ganze Ausgabe und einzelne Beiträge als PDF-Downloads im Design der Print-Ausgabe VOLLSTÄNDIGE AUSGABE PDF, 80 Seiten 5 MB [Download

Earlier this year, we published The Great Flattening. Our argument was straightforward: AI is making every company sound the same.

A recent Stanford study found the more people use AI, the more convinced they it is saving them time… when the results show it isn't and the price of that illusion is about to get a lot more visible for organizations. The researchers tested nearly 2,700 people on exactly the kinds of tasks most Microsoft Copilot rollouts are built around. Summarization Rewriting Simple information retrieval Basic arithmetic Short drafting tasks Here's what the results found. --> People used AI far more than they predicted… 47% of the time versus the 33% they expected --> They believed AI would save roughly 56 seconds per task. It saved about 7 --> On the simplest tasks, AI actually made things slower. --> And after using AI, people became even more likely to use it again — and more confident it was creating value — regardless of whether it actually was. This isn’t efficiency. That's a belief of it. And it's costing organizations. Most AI transformation programs are built on a simple assumption… Train people. Increase adoption. Drive usage. And the value will follow. This study suggests something uncomfortable: The tools and metrics many organizations are using to demonstrate progress may not be measuring value at all. Usage may simply be creating more usage. For the past two years, that distinction barely mattered. AI was effectively subsidized. Uber recently disclosed it burned through its entire 2026 AI budget in just four months. Its COO admitted it's still difficult to draw a direct line between rising AI costs and customer value. Uber won't be the last organization facing that question. The discounts they received to adopt those tools are quicky disappearing. And leaders who built their business cases around adoption metrics are about to face a harder question: Where is the business value? Measuring AI activity is easy: Training completion Adoption Active users Prompt volume Measuring AI transformation is much harder: Workflow redesign Decision quality Knowledge architecture Operational change One creates dashboards. The other changes how work gets done. And that's where many organizations are stuck. Not deciding whether AI works. Deciding whether they're willing to fund the foundations that allow more than a handful of strategic projects to succeed. The foundation that creates lasting value isn't another tool. It's the semantic layer, decision logic, governed platforms, and embedded expertise that enable teams to redesign work instead of simply accelerating it. Usage without those conditions doesn't become transformation. It becomes habit. The organizations that scale AI won't be the ones with the highest adoption rates. They'll be the ones willing to fund the foundations that make deep operational redesign repeatable. | 24 comments on LinkedIn

PodHive AI als NotebookLM-Ersatz – ein EU-Anbieter im PraxistestIn dieser Folge schaue ich mir PodHive.ai an: ein Tool aus Bonn, das Audio-Podcasts aus eigen...

KI-Kennzeichnung wird Pflicht: Was Unternehmen ab 2026 zu Deepfakes, KI-Texten, Chatbots, Fristen und Bußgeldern wissen müssen.

The most important HR research of 2024–2026 converges on one uncomfortable truth: the AI disruption of HR is not a tech implementation challenge. It is a people strategy crisis — and many organizations are solving entirely the wrong problem.

Last week I ran three roundtables at Offbeat Fest on a question I keep coming back to: How do you design learning experiences that actually drive behaviour change? It was a great event and I really enjoyed the roundtables and meeting all the wonderful people. Thanks to everyone who showed up and brought their thinking! Something that came up again and again in the conversations is that for what is taught to be applied, it needs to be baked into the design of the learning experience from before learners arrive, during the experience and of course, after. The perspective of how will we ensure that people apply what we're teaching them needs to have more focus in the research/design phase. Here's what the room surfaced, mapped across three phases: PRE: Prepare the human 1. Scope the real problem first. "What's in it for me?" is the right question to start with. 2. Choose the right messenger. Who delivers it shapes commitment. 3. Create curiosity, break the script. Spark, don't lecture. 4. Start accountability early. Two-way expectations before the experience begins. DURING: Less content, more conditions 1. Less content. The "aha" needs room to land. 2. Make it relevant and needed. Structural, not cosmetic. 3. Psychological safety, throughout. Maintained, not just declared. 4. Human-centric, theory-anchored. Honesty about the discomfort real learning produces. POST: Make application visible 1. What isn't witnessed doesn't stick. 2. Accountability partners. Social, not solitary. 3. Keep connections alive. The relational network sustains momentum. 4. Rhythm of reflection. Cadence beats intensity. 5. Treat Post as a design space. Most programmes go dark here. | 13 comments on LinkedIn

𝘼𝙧𝙚 𝙬𝙚 𝙪𝙣𝙡𝙚𝙖𝙧𝙣𝙞𝙣𝙜 𝙝𝙤𝙬 𝙩𝙤 𝙩𝙝𝙞𝙣𝙠? Anthropic surveyed over 81,000 people globally to understand their real expectations from AI. The top responses are deeply human: Professional Excellence: 18.8% Time Freedom: 11.1% Learning & Growth: 8.4% On the surface, technology delivers: 32% experience immediate productivity breakthroughs and nearly 10% feel a direct lift in learning. 𝗕𝘂𝘁 𝘁𝗵𝗲 𝗯𝗮𝗰𝗸𝗹𝗮𝘀𝗵 𝗶𝘀 𝗶𝗺𝗺𝗲𝗱𝗶𝗮𝘁𝗲  16.3% point to a dark reality: 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗔𝘁𝗿𝗼𝗽𝗵𝘆. This is not a vague prediction. Educators and academics witness this intellectual decline 2.5 to 3 times more frequently than the average participant. 𝗪𝗵𝘆 𝗶𝘀 𝘁𝗵𝗶𝘀 𝗵𝗮𝗽𝗽𝗲𝗻𝗶𝗻𝗴?  In corporate learning, AI is currently being abused as a cheap shortcut. Companies flood their systems with low-effort AI content, fueling an illusory productivity that leaves 18% of users deeply concerned. Employees blindly click through generated slides, offload critical thinking to machines, and enter the workplace as "human hard drives" - stuffed with rote memorization but entirely lacking real capability. 𝗧𝗵𝗲 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: 𝗦𝘁𝗼𝗽 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗻𝗴 𝘁𝗵𝗲 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀. Technology only unfolds its true value when it helps us understand deeper, not when it replaces our intellect. The true learning tutor is not generative AI, but a biologically accurate, adaptive algorithm: ➡️ Time as a variable, competence as a constant:  It deconstructs rigid, time-based benchmarks and tailors the path in real-time to the learner's unique needs. ➡️ Zero Blind Spots:  It uncovers dangerous unconscious incompetence and drives every single employee to documented 100% proficiency. ➡️ Brain Training over Content Spam:  While generative AI accelerates content production, our algorithm ensures the human brain remains actively challenged and developed. 👉 Find deeper data, graphics, and the full qualitative breakdown of this global study in the attached PDF slideshow.

Aligning agent work with human will — 35 professions, graded against 2,066 fact-anchored criteria, evaluated by what workers actually want automated.

I just read all 245 paragraphs of the Pope’s encyclical on AI. Here are 8 fantastic ideas (even if you aren't religious). The Pope just released a major document on artificial intelligence. It's called Magnifica Humanitas. Cards on the table, I’m not a Catholic. I am Christian though. But even if you aren’t, good thinking is good thinking. And this is some of the clearest writing on AI I've read all year. So here are the 8 ideas that stood out: 1. We don't get to opt out of this moment The encyclical refuses to treat AI as "merely yet another theme to be studied or a crisis to be managed." It's already here. Pretending it’s not happening or that it's someone else's problem is not a responsible option. 2. We are building something. The only question is what. This is the heart of the whole document. The Pope uses two images from the Bible. Babel: a tower built on pride and efficiency that ends in confusion. And Jerusalem: a city rebuilt "piece by piece" through shared responsibility. "The primary choice is not between a 'yes' or 'no' to technology, but rather between constructing Babel or rebuilding Jerusalem." 3. Technology is never neutral. "Technology is never neutral, because it takes on the characteristics of those who devise, finance, regulate and use it." I've said this for years, borrowing from Neil Postman. Good to see it stated so plainly. 4. AI is "cultivated," not "built (and we don’t really understand it) Even the people who design these systems have "only a limited understanding" of how they actually work. Nobody is fully in control. That should make all of us a little more humble. 5. Don't confuse this "intelligence" with yours. AI systems "merely imitate certain functions of human intelligence." They "do not undergo experiences, do not possess a body, do not feel joy or pain." They can simulate empathy without having any. The word "intelligence" may smuggle in ideas that simply aren’t true. 6. "Having more" is not "being more." Progress without moral growth produces "an increase in means without a growth in humanity." More capability does not automatically mean a better life. Having more is not an automatic good. 7. Watch who holds the power. AI "tends to amplify the power of those who already possess economic resources, expertise and access to data." A small number of private actors increasingly set the rules. That concerns everyone, believer or not. 8. A civilisation is judged by its care, not its power. Here’s my favourite line in the whole document: "The quality of a civilization is measured not by the power of its means, but by the care it is able to offer, by its ability to recognize the other as a face not merely as a function." Wow. And readily applicable to the classroom. Overall, this was a great read. I commend it to you regardless of your faith position. I’ve included a link in the comments below. Which of these ideas resonated with you most strongly? Let me know! | 117 comments on LinkedIn