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Siara KI als technische Unterstützung passt ideal 😊

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.

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

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

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

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.

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.

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

Fosway Group, in partnership with Learning Technologies, shares the second cluster of research from Digital Learning Realities 2026 - AI Value.

Every technology wave eats its own vocabulary. Words start sharp. Then the market discovers them. “Cloud.” “Platform.” “Transformation.” “AI.” At first, each points at something real. Then it becomes useful in a sales deck. Then it becomes useful in every sales deck. The word survives. The meaning does not. So here is Seale’s Law: ⚡ The precision of a word’s meaning is inversely proportional to its commercial value as a marketing term ⚡ It is a joke - but only just. The painful irony is that this is now happening to semantics. For years, those of us working with ontologies and knowledge graphs have made a fairly unfashionable argument: meaning matters. Context matters. Data is not ready for machines simply because it has been loaded into a warehouse, lakehouse, dashboard or API. When concepts remain implicit, identities are inconsistent and business meaning is trapped inside departmental dialects, the system may appear integrated. It is not. AI has made that impossible to ignore. LLMs have exposed a truth that many analytics programmes could postpone: enterprise data is not connected enough, described clearly enough or governed around meaning well enough for intelligent systems to use it safely. AI does not simply need more data. It needs data with identity, relationships, context and explicit business meaning. That is good news. It is also dangerous. 🔵 “Semantics” is about to become a beautiful container for vague promises. The word will be attached to products, features, copilots, roadmaps and platforms. Some of it will be real. Much of it will be rebranding. Organisations moving quickly will be sold noise at precisely the moment they need clarity. The message is simpler than the market will make it. 🔵 Every organisation has a data integration problem. To solve it for AI, you must connect your data and organise that connected data through an ontology: an explicit model of the things your organisation cares about, what they mean and how they relate. That is phase one: internal coherence. 🔵 Phase two is arriving fast: interoperability. No organisation operates alone. Supply chains, regulators, partners, customers, research networks and financial systems all cross organisational boundaries. If AI is going to work across those boundaries, meaning has to travel too. That requires open standards. Not proprietary semantic theatre. Not another silo with “ontology” printed on the brochure. Open standards for identity, connectivity and shared models of meaning. Semantics is having its moment. Our task is to protect the substance before the market hollows out the word. Your AI strategy is only as strong as your semantic foundation. Connect the data. Model the meaning. Use open standards. Prepare for interoperability. And if Seale’s Law is right, do it before we need a new word for the thing that finally made the old one profitable. 🔗 Building your semantic foundation? Let's talk: https://lnkd.in/ezHU2amU | 48 comments on LinkedIn

The Summer 2026 Edition

I freakin' love studies that match with reality - Boston Consulting Group (BCG) just nailed it with their new AI at Work Survey. These are three trends we see in real life, all the time. Let's go through them. TIME SAVED ISN'T THE SAME AS VALUE CREATED BCG found that 42% of frontline employees who use AI regularly save a full day or more per week. But 66% get little or no guidance on what to do with that time, and more than half never redirect it to strategic work. This is our experience as well - including the biggest company we've ever worked with (a Fortune 5). They were seeing gradual productivity gains with AI - which is great! But there was a problem. They weren't finding the radical gains that leadership knew AI could produce. They invited us to come in and investigate why they weren't seeing the kind of sharp gains AI is capable of producing. The culprit was a very human one. Seems that when people get time back, they don't redirect it to innovation. They just keep filling their time up with more emails. Getting to ROI means recognizing that productivity can be the enemy of innovation. Time redeployment is the responsibility of leadership. (I wrote a newsletter on this called The Million Little Hockey Sticks.) USE CASES ARE A FRAGMENT, PROCESSES ARE THE WHOLE BCG found that companies that redesign workflows end-to-end outperform the ones that just deploy tools. Invent initiatives have nearly doubled since 2025. This is one of the reasons why we don't do use cases, we do processes. A use case is a fragment of a workflow. You have to think about why that process exists in the first place, how it drives value, start with first principles, and then imagine you have a brilliant new colleague (or 10) to help you. You have to think big, not small. UPSKILLING ISN'T A KNOWLEDGE PROBLEM BCG found 88% of respondents believe they'll need major upskilling in the next five years, yet only 36% feel properly trained. The problem isn't that leadership doesn't care about upskilling. It's that we keep believing this is a proficiency to learn rather than a set of habits and behaviors to master. That's why we call it mindset. Because it is not 'proficiency.' It's not 'competency.' Still skeptical? Just know that Anthropic, McKinsey & Company, KPMG US and many others are using that language: Behavior, not proficiency. Microsoft ran an entire research project showing this was the case (that actually proved our methodology was 2x as effective in producing quality work, but whatever.) Redesigning how you work is everything. The focus, as BCG says, must be on strategy, not on the tool. The tools matter way less than we think. Strategy over tools. Well said, BCG. | 68 comments on LinkedIn

Otto Scharmer has been helping leaders' sensemaking for decades. His insights are extraordinarily relevant in the AI era. Here he proposes four levels of collective action and strategic innovation, each with its own structure of attention. In this powerful article he starts with the same thesis as in my book Thriving on Overload: "Overwhelm has become a shared planetary experience." The key danger is intelligence monoculture, "the assumption that AI is the only intelligence worth investing in." He identifies three forms of intelligence: Artificial Intelligence: Predicting from patterns Organic Intelligence: Understanding through relationships Source Intelligence: Creating from emergence The four levels of collective action each require different leadership styles 🪜 Level 1.0: Pattern-Executing — Automating AI automates well-defined tasks, with leaders focused on delegation, oversight, and ensuring reliable execution. 🪜 Level 2.0: Pattern-Adapting — Augmenting Humans and AI work together to adapt existing approaches, with leaders providing judgment, context, and direction. 🪜 Level 3.0: Pattern-Shaping — Co-Sensing People and AI combine insights to understand complex systems, challenge assumptions, and shape new possibilities through collaborative sensemaking. 🪜 Level 4.0: Pattern-Originating — Deep Sensing and Cocreating Leaders create the conditions for breakthrough ideas and collective creativity to emerge, with AI supporting rather than driving the process. The fundamental leadership choice is the allocation of attention and budget across the levels. If there is insufficient attention at the higher levels, the organization's ability to create distinctive value will inevitably erode. Otto is absolutely right. Amid the extraordinary AI transition, leaders need to step back to truly see the big picture, and ensure that organic and source intelligence remain central to how organizations create and fulfil their vision and purpose.

Seven firms published AI reports this year.   None blamed the technology. Financial services leaders will recognise the diagnosis: programme governance. I read all seven back to back this week. McKinsey, IBM, Bain, BCG, Accenture, Anthropic, OpenAI.   Different research teams, different client bases, same conclusion. The operating model is the bottleneck, not the model, not the compute, not the vendor. Every one of them names the same gap.   Different language, same root cause. Decision rights aren't defined.   BCG found 58% of leading firms expect governance and decision rights to change under agentic AI. Most haven't started that work. Accountability isn't mapped.   When an agent makes a consequential call, three teams claim it and none of them own it. Data governance isn't there yet.   Agent output can't be verified, audited, or trusted at scale. 🚀 Subscribe to The Transformation Constant — my newsletter — https://lnkd.in/d_mtyWzr The firms actually scaling this in financial services and asset management aren't moving faster than everyone else. They did the boring work first: decision rights, an AI governance framework, and a data foundation that can survive an audit. Everyone else is running pilots on an operating model nobody redesigned to carry them. AI shows the cracks.   It does not fill them. The full 2026 agentic AI strategy pack, all seven reports: 1️⃣ McKinsey: The Agentic Organisation   ↳ Five dimensions that decide whether AI agents create value or just noise: strategy, operating model, technology, talent, and governance.   https://lnkd.in/evEzBQh8 2️⃣ IBM: Agentic AI Operating Model   ↳ Why efficiency-first AI rollouts stall, and the execution systems that turn agents into structural advantage instead.   https://lnkd.in/diWGccJr 3️⃣ Bain: Foundations for Agentic AI   ↳ The unglamorous work that actually determines scale: data readiness, governance, and execution discipline, before the agent goes live.   https://lnkd.in/ePgxJBqH 4️⃣ BCG: Leading in the Age of AI Agents   ↳ When an agent makes the call, who owns it. BCG's framework for closing the accountability gap before it opens.   https://lnkd.in/eCV-x6wM 5️⃣ Accenture: Agentic AI Platform Strategy   ↳ Why agents force a rebuild of platform strategy and vendor decisions, not just another layer of tooling.   https://lnkd.in/e_RWUZfr 6️⃣ Anthropic: State of AI Agents (2026)   ↳ Guardrails that scale with the agent, not against it. Built for regulated environments, governance-first.   https://lnkd.in/e4w3FnRb 7️⃣ OpenAI: State of Enterprise AI   ↳ Where enterprise AI value is actually showing up right now, measured from real usage data, not demos.   https://lnkd.in/eqQHwZd8 🔔 Follow Justin R. for more Enterprise Transformation content | 169 comments on LinkedIn

Vor zwei Wochen hatte ich auf LinkedIn gefragt, welches Thema euch aktuell am meisten interessiert. Future Skills lag klar vorn, also zeige ich in diesem Vid...

«Ist es eigentlich schlimm, wenn ich ChatGPT für die Schule nutze?»

🚀 Mercedes-Benz bündelt HR und Konzern-IT in einem Vorstandsressort: 𝗣𝗲𝗼𝗽𝗹𝗲 & 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗧𝗲𝗰𝗵.

Irgendetwas ist gerade anders.

Deutschland hat ein eigenes offenes KI-Modell: Soofi S, trainiert in der Telekom-Cloud, unterstützt vom Bundeswirtschaftsministerium.