Open New Learning Lab Resources

Implementing AI isn't just about integrating new technology. It's about transforming the way people work. Without a structured change management approach, AI initiatives can face resistance, underperformance, and missed opportunities. Here are the key success factors for good AI change management: ✅ Clear vision and purpose: Define the goals and benefits of AI in your company. Make it aspiring. ✅ Strong leadership: Leaders must champion the change and guide their teams. Don't forget to communicate clear expectations. ✅ Effective communication: Regular updates and transparency to manage expectations and take away fear. ✅ Evangelists: Elect "Pro users" in each department to provide instant support. ✅ Training and development: Equip your team with the skills needed to leverage AI. ✅ AI platform: Ensure the platform has excellent UX to drive user adoption and engagement. UX is not UI! ✅ AI agents: Yes, this specific feature is key. Create and share AI agents that employees can use off the shelf. Significantly improves adoption! At Zive, it's our mission to make work efficient and enjoyable for everyone. We provide the best platform for it. But it doesn't work without good change management. Thankfully, we have amazing partners who support our customers with it. #AI #ChangeManagement #Leadership #Innovation #EmployeeEngagement #BusinessTransformation #UserExperience #AIagents | 18 comments on LinkedIn

Watching long videos but not learning much? Here’s my system: 1) Upload a video you watched/are… | 61 comments on LinkedIn

KI-Anwendungen aufnehmen und klassifizieren: Aber wie? Der AI Act verlangt eine Risikoklassifizierung von KI-Anwendungen und Systemen im Unternehmen. Dafür…

Friends, this is the MOST IMPORTANT study on AI in 2025. The brilliant Ethan Mollick and team studied how AI impacts individuals and teams across Procter & Gamble- the results are stunning. Here’s what you need to know: The "Cybernetic Teammate" study was conducted in Summer 2024 by a research from Harvard and Wharton, in partnership with Procter & Gamble.  ++++++++++++++++++++ WHO WAS TESTED: The study involved 776 P&G professionals and replicated P&G's product development process across four business units. The experiment featured four distinct conditions: - Individuals working alone without AI - Individuals working alone with AI - Teams of two specialists (one commercial expert, one technical R&D expert) working without AI - Teams of two specialists working with AI ++++++++++++++++++++ KEY FINDINGS: INDIVIDUAL PERFORMANCE: AI improved individual performance by 37% TEAM PERFORMANCE: AI improved team performance by 39% BREAKTHROUGH SOLUTIONS: Teams using AI were 3x more likely to produce solutions in top 10% of quality EFFICIENCY GAINS Individuals using AI completed tasks 16.4% faster than those without Teams with AI finished 12.7% faster than teams without AI OUTPUT QUALITY Despite working faster, AI-enabled groups produced substantially longer and more detailed solutions EXPERTISE AND COLLABORATION EFFECTS Breaking Down Silos!! Without AI: Clear professional silos existed — R&D specialists created technical solutions while Commercial specialists developed market-focused ideas With AI: Distinctions virtually disappeared — both types of specialists produced balanced solutions integrating technical and commercial perspectives EXPERIENCE LEVELING: Less experienced employees using AI performed at levels comparable to teams with experienced members EMOTIONAL EXPERIENCE Positive Emotions: AI users reported significantly higher levels of excitement, energy, and enthusiasm Negative Emotions: AI users experienced less anxiety and frustration during work Individual Experience: People working alone with AI reported emotional experiences comparable to or better than those in human teams TEAM DYNAMICS Solution Types: Teams without AI showed a bimodal distribution (either technically or commercially oriented solutions) Balanced Input: AI appeared to reduce dominance effects, allowing more equal contribution from team members Consistency: Teams with AI showed more uniform, high-quality outputs compared to the variable results of standard teams We'll be talking about this study for a while. +++++++++++++++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your company is ready, we are ready to upskill your workforce at scale. Our Generative AI for Professionals course is tailored to enterprise and highly effective in driving AI adoption through a unique, proven behavioral transformation. Check out our website or shoot me a DM. | 133 comments on LinkedIn

In dieser Episode berichtet Dr. Bettina Laugwitz, die bei SAP das Thema KI-Ethik verantwortet, von SAPs Ansatz zur ethischen Entwicklung und Implementierung von künstlicher Intelligenz.

Sehr spannende Studie von der Harvard Business School (03/2025) die aufzeigt, dass der Einsatz generativer KI (GenAI) die zentralen Aspekte von Teamarbeit…

In this podcast I describe the massive AI-powered revolution starting in corporate L&D, and give you some history, context, and a mandate for the future. Thi...

One of my favourite reads from the last six months is Sequoia Capital’s report exploring the evolution of generative AI and it’s implications for the messy…

"𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀!" 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗗𝗿𝗲𝗮𝗺: ➜ Deploy AI Agents ➜ Automate everything ➜ Enjoy efficiency 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 𝗥𝗲𝗮𝗹𝗶𝘁𝘆: ➜ Messy, siloed, unreliable data ➜ AI hallucinations & compliance nightmares ➜ Enterprise AI initiatives stall as organizations spend more time fixing data issues than realizing AI-driven value. The Hard Truth: AI (agents) aren't failing—data strategies are. AI Agents are only as effective as the data beneath them. Without governed, high-quality data, AI adoption becomes an expensive experiment instead of a strategic advantage. Important to fix the data first. Kudos for this image to Armand Ruiz! | 258 comments on LinkedIn

We’re evolving our platform to help developers and enterprises build useful and reliable agents.

Das KI-System "The AI Scientist-v2" von Sakana AI hat eine wissenschaftliche Publikation erstellt, die den Peer-Review-Prozess bei einem Workshop der wichtigen…

🌟 KI-generierte Inhalte und Urheberrecht: Wer haftet bei Rechtsverletzungen? Stell dir vor, du nutzt eine generative KI, um ein Marketing-Bild zu erstellen.… | 16 comments on LinkedIn

Wer einen Blick in die Kristallkugel bzgl. KI Agenten wagen möchte - seit wenigen Tagen geistert Manus AI durch meinen Feed. Warte noch sehnsüchtig auf den Access, aber was man bereits sehen kann in diversen Demos macht richtig Lust drauf: General AI Agent als quasi Mischung aus Claude Computer Use, Chat GPT Operator, Deep Research etc und das ganze extrem intelligent miteinander verknüpft. "Manus" als die KI "Hand", die einem tatkräftig im digitalen Alltag hilft. 💪

Thought is a multi-step process, but rarely linear. Early LLMs lacked structured reasoning and often struggled with logic. Chain-of-Thought introduced…

🚂 Erfahrt in der neuen AI 4 L&D Podcastfolge, wie DB Lingua die Kommunikation und Effizienz zwischen Fahrdienstleitern und Triebfahrzeugführern verbessert u...

*** 🚨 Discussion Piece 🚨 *** Is it Time to Move Beyond Kirkpatrick & Phillips for Measuring L&D Effectiveness? Did you know organisations spend billions on Learning & Development (L&D), yet only 10%-40% of that investment actually translates into lasting behavioral change? (Kirwan, 2024) As Brinkerhoff vividly puts it, "training today yields about an ounce of value for every pound of resources invested." 1️⃣ Limitations of Popular Models: Kirkpatrick's four-level evaluation and Phillips' ROI approach are widely used, but both neglect critical factors like learner motivation, workplace support, and learning transfer conditions. 2️⃣ Importance of Formative Evaluation: Evaluating the learning environment, individual motivations, and training design helps to significantly improve L&D outcomes, rather than simply measuring after-the-fact results. 3️⃣ A Comprehensive Evaluation Model: Kirwan proposes a holistic "learning effectiveness audit," which integrates inputs, workplace factors, and measurable outcomes, including Return on Expectations (ROE), for more practical insights. Why This Matters: Relying exclusively on traditional, outcome-focused evaluation methods may give a false sense of achievement, missing out on opportunities for meaningful improvement. Adopting a balanced, formative-summative approach could ensure that billions invested in L&D truly drive organisational success. Is your organisation still relying solely on Kirkpatrick or Phillips—or are you ready to evolve your L&D evaluation strategy? | 34 comments on LinkedIn

2025 is the Year of LCMs and not LLMs.   Meta has announced a new architecture for the future of Large Language Models called Large Concept Models.   Building… | 103 comments on LinkedIn

Tolle Grafik zu KI-Agenten! Insbesondere in den USA gibt es viele Beteiligte mit Tech-Hintergrund, die super Abbildungen erstellen und damit nicht selten sehr hohe Reichweiten erzielen. Genau diese Art der Wissenschaftskommunikation kommt besonders gut an. Zum Linkedin-Post https://lnkd.in/eq4mWQFd

Free AI Writing, PDF, Image, and other Online Tools - TinyWow

Early in my facilitation career, I made a big mistake. Spent hours crafting engaging activities and perfecting every little detail… Thinking that amazing… | 46 comments on LinkedIn

Lessons Learned After 6 Months of Building AI Agents as a Non-Programmer (without the hype)👨‍💻 I just released a 43-minute deep dive on YouTube, breaking down what I’ve learned, the mistakes I’ve made, and cutting through the AI agent hype to share real insights on building AI systems—without a coding background. It’s a crazy time we’re living in. The barrier to entry for AI automation has never been lower, and no-code tools like n8n let anyone build AI-powered workflows faster than ever. 🧠Here are the 7 key lessons I’ve learned: 1️⃣ Build workflows first before jumping into AI agents. Many problems can be solved with simple rule-based automation. 2️⃣ Always wireframe before building. Skipping this step is like trying to solve a puzzle without seeing the picture. 3️⃣ Context is everything. AI performance depends on predefined logic, user context, and real-time data. 4️⃣ Know when NOT to use a vector database. They aren’t magic, and structured data is often better suited for relational databases. 5️⃣ Prompting AI agents is an art. Write your own prompts and refine them reactively—don’t rely on auto-generated prompts. 6️⃣ Scaling AI agents is a nightmare. A few hallucinations may not matter for one user, but at scale, they become huge problems. 7️⃣ No-code tools like n8n are powerful, but they have limits. They’re great for MVPs and internal automations, but scaling sometimes requires a hybrid approach with custom code. 🔗 Watch the Full Video Here: https://lnkd.in/gArmrd8p 🔗 Join the Best Community to Learn How to Build No-Code AI Agents: https://lnkd.in/dqVsX4Ab | 28 comments on LinkedIn

An article in Nature Human Behaviour examines the cognitive and emotional reasons behind people’s resistance to AI tools, even when these tools could be beneficial. The authors, structure their analysis around five key psychological barriers: 1. Opacity – Many AI systems function as “black boxes,” meaning their decision-making processes are difficult to interpret. This lack of transparency fosters distrust, as users struggle to understand or predict AI behaviour. To address this, some AI-powered products now prioritise explainability. One example is Netflix recommendations, which provides explanations such as “We suggest this movie because you watched Don’t Look Up.” 2. Emotionlessness – AI lacks human emotions, making interactions with it feel impersonal and detached. People often prefer human decision-makers because they perceive them as capable of empathy, care, and moral reasoning. 3. Rigidity – AI operates based on predefined rules and patterns, which can make it appear inflexible or incapable of handling nuanced, context-dependent situations in the way humans can. 4. Autonomy – The idea that AI acts independently can create discomfort, as it raises concerns about control, agency, and the unpredictability of automated systems. This becomes particularly important for activities through which we express our identity. People are more trusting of AI in situations where they don't seek agency. 5. Group Membership – Humans have a natural tendency to trust other humans over non-human agents. AI is often perceived as an “outsider,” which can lead to resistance, particularly in domains where social interaction or human judgment is highly valued. The article discusses how these psychological barriers are deeply rooted in human cognition and biases, drawing on empirical studies that show both correlational and causal links between these factors and AI resistance. The authors also separate the barriers into two categories: - AI-related factors (e.g., a system’s lack of transparency or inability to convey emotions) - User-related factors (e.g., cognitive biases, emotional responses, and cultural influences shaping AI perception) This distinction is important for designing interventions that promote the adoption of beneficial AI tools. However, the authors warn that efforts to overcome AI resistance, for example by including anthropormorphic features, could have unintended consequences. | 28 comments on LinkedIn

BREAKING! ChatGPT 4.5 is here! And it feels a bit magical. My favorite part is the increased EQ - check out the highlight video below. This feels like the future of AI. More Claude-y, which I LOVE. Empathy is exactly what I've been missing from ChatGPT! It's been fine - but this is another level. This is how we will actually communicate with AI. It's the thing I love about Claude. Microsoft is leaning into empathy with Mustafa Suleyman after his turn at Inflection. Okay, let's get into it. BTW - the video is MY OWN EDIT. I just loved the EQ example so much. HIGHLIGHTS: EXCLUSIVE ACCESS: Initially available only to $200/month Pro subscribers, coming to Plus users next week MOST HUMAN-LIKE YET: Features significantly enhanced emotional intelligence and conversational abilities LARGEST MODEL: OpenAI's biggest model to date, though specific parameters remain undisclosed FINAL PRE-REASONING MODEL: Last major release before OpenAI introduces chain-of-thought reasoning in GPT-5 >>The Evolution of AI Conversation What stands out with 4.5 is how much more human the interactions feel. The model demonstrates substantially improved emotional intelligence, with responses that show greater nuance and sensitivity. This shift toward a more empathetic, Claude-like conversation style suggests OpenAI is recognizing that raw intelligence isn't enough – it matters HOW it talks to you. >>Key Features That Make It Special Enhanced Knowledge and Reasoning - The expanded knowledge base means deeper, more comprehensive answers - Significantly fewer hallucinations, making it more reliable for critical tasks - Pattern recognition that borders on intuitive understanding Reimagined User Experience - Conversations flow naturally, without the mechanical feel of earlier models - Context handling that actually remembers what you've been discussing - Lightning-fast responses despite its massive size >>The Price of Progress Access to this cutting-edge technology comes at a premium. ChatGPT Pro subscribers ($200/month) get first access, with Plus users ($20/month) joining the party the week of March 3. Enterprise and Education users will follow shortly after. >>Where 4.5 Really Shines The model particularly excels at: - Creative writing with genuine emotional depth - Complex problem-solving that requires nuanced understanding - Communication tasks where tone and empathy matter - Multi-step planning and execution, especially for coding workflows Pretty cool. ++++++++++++++++++++ UPSKILL YOUR ORGANIZATION: When your company is ready, we are ready to upskill your workforce at scale. Our Generative AI for Professionals course is tailored to enterprise and highly effective in driving AI adoption through a unique, proven behavioral transformation. It's pretty awesome. Check out our website or shoot me a DM.

➡️ Was sagt die aktuelle IAP Studie 2024? Das ZHAW IAP Institut für Angewandte Psychologie hat Fach- und…

We also… | 23 comments on LinkedIn

🔍 Deep Dive in die Welt der Deep Research Modelle von OpenAI, Perplexity und co! 🌐Welches davon, macht den besten Eindruck?In der dynamischen Kontext der K...

This is what's keeping me up at night these days... 1. google has become unusable. once you get used to "deep research" (thanks grok, perplexity etc), google… | 251 comments on LinkedIn

Wie können wir KI in den unterschiedlichsten Jobs und Bereichen einsetzen, um unser Arbeitsleben zu verbessern? In diesem Video schauen wir uns genau das an!...

17x interne KI Chatbots / CompanyGPTs eingeführt. Diese 11 Lektionen habe ich gelernt: 𝗗𝗮𝘀 "𝗪𝗮𝗿𝘂𝗺" 𝘇𝗮̈𝗵𝗹𝘁. Was ist das Ziel der KI-Initiative?… | 54 Kommentare auf LinkedIn

🔍🌟 Entdecke, wie du mit einem selbstgebauten KI-Assistenten deinen Arbeitsalltag als Learning Professional vereinfachen kannst! In diesem Video zeige ich d...