LinkedIn AI Content

We’re living through one of the biggest workforce shifts since the dawn of the internet — and most people feel like they’re already behind. Every headline screams the same thing: “AI is taking over jobs.

🥇This is Gold! just dropped by Carnegie Mellon University! It’s one of the most honest looks yet at how “autonomous” agents actually perform in the real world. 👇 The study analyzed AI agents across 50+ occupations, from software engineering to marketing, HR, and design, and compared how they completed human workflows end to end. What they found is both exciting and humbling: • Agents “code everything.” Even in creative or administrative tasks, AI agents defaulted to treating work as a coding problem. Instead of drafting slides or writing strategies, they generated and ran code to produce results, automating processes that humans usually approach through reasoning and iteration. • They’re faster and cheaper, but not better. Agents completed tasks 4 – 8× faster and at a fraction of the cost, yet their outputs showed lower quality, weak tool use, and frequent factual errors or hallucinations. • Human–AI teaming consistently outperformed solo AI.🔥 When humans guided or reviewed the agent’s process, acting more like a “manager” or “co-pilot”, the results improved dramatically. 🧠 My take: The race toward “fully autonomous AI” is missing the real opportunity, co-intelligence. Right now, the biggest ROI in enterprises isn’t from replacing humans. It’s from augmenting them. ✅ Use AI to translate intent into action, not replace decision-making. ✅ Build copilots before colleagues, co-workers who understand your workflow, not just your prompt. ✅ Redesign processes for hybrid intelligence, where AI handles execution and humans handle ambiguity. The future of work isn’t humans or AI. (for the next 5 years IMO) It’s humans with AI, working in a shared cognitive space where each amplifies the other’s strengths. Because autonomy without alignment isn’t intelligence, it’s chaos. Autonomous AI isn’t replacing human work, it’s redistributing it. Humans shifted from doing to directing, while agents handled repetitive, programmable layers. Maybe we are just too fast to shift from "uncool" Copilot to sth more exciting called "Fully Autonomous AI", WDYT? | 72 comments on LinkedIn

AI doesn’t need hype. It needs hygiene. This successful post with 2k likes is from CA member Clare Kitching 🔥 Original post below: ⬇️ ⬇️ ⬇️ AI doesn’t need hype. It needs hygiene. Up top, the dream is glossy:  GenAI, agentic AI, digital twins, robotic workers. But below the surface?  Data silos. Technical debt. Legacy systems. Manual processes. App sprawl. Weak governance. No wonder AI pilots stall. Right now, AI feels like a race, with everyone sprinting toward automation glory. But most “AI problems” aren’t really AI problems. They’re data, integration and process problems. If your data is messy, your systems don’t talk, and your processes are outdated, then no algorithm will save you. It’s like dropping a turbo engine into a car that’s never had an oil change. You’ll go fast, but only for a few seconds. Before automation, fix the basics: → Understand your processes → Build a robust data architecture → Establish clear governance → Create smooth integrations Then start small. Pick one domain. Prove value. Learn fast. In parallel, tackle technical debt, strengthen governance and modernise integrations. And make sure your cybersecurity is as advanced as your AI ambitions. Keep the hype in check. Not every “agentic” demo is enterprise-ready. What’s the first foundation you’d fix to make AI actually deliver value in your organisation? ♻️ Repost this to your audience. Follow The Creator Accelerator by Chris Donnelly for more. | 46 comments on LinkedIn

The Ultimate AI Tools Cheat Sheet is here! 🤖 - 12 use cases, 48 tools, everything you need for 2024. 🔥 - Credit: zumersultana on Twitter/X ------------------------------------------------------------------- 👉 Checkout our 100K+ AI community and learn AI in 3 minutes a day for $0, along with 17+ Free AI resources. ⬇️ 👉 Visit AI PlanetX for more AI insights ( AIPlanetX. Com ) -------------------------------------------------------------------- #AI #chatgpt #aiart #openai #productivity #business

Stop wasting hours thinking of how to prompt ChatGPT. You’re missing out. Your competitors aren’t. This changes that. Here are 30 prompt templates that will make ChatGPT work like your growth machine. 1. Detailed Instruction Template "Give me detailed, step-by-step instructions on how to [perform a task]." Example: "Give me detailed, step-by-step instructions on how to set up a home Wi-Fi network." 2. Role Play Specialist Template "Act as a [role/expert]. Help me with [problem/situation]." Example: "Act as a tax accountant. Help me with filing self-employment taxes." 3. Explain Like I’m Five (ELI5) Template "Explain [concept] to me as if I were five years old." Example: "Explain blockchain technology to me as if I were five years old." 4. Comparison Table Template "Create a table comparing [items or concepts] based on [criteria]." Example: "Create a table comparing electric vehicles, hybrids, and gasoline cars based on price, range, and emissions." 5. Pros and Cons List Template "List the pros and cons of [topic/decision/technology]." Example: "List the pros and cons of working remotely full-time." To know more prompt templates, Check the infographic below 👇 What other prompt templates would you recommend adding here? Comment below 👇 Learn AI for free: https://lnkd.in/e4bhG8-i  ♻️ Share this to help your network use ChatGPT better | 36 comments on LinkedIn

ChatGPT vs Gemini vs Claude vs Grok vs DeepSeek. Everyone’s asking, “Which one’s the best?” Truth is: there’s no single winner. Each AI has its own lane. Some are creative powerhouses. Others are built for deep research, real-time updates, or scalability. Choosing the right model isn’t about hype, it’s about fit. Here’s your quick guide 👇 - ChatGPT The all-rounder. Brilliant for text, audio, video, code, and workflow automation. - Gemini Perfect for Google Workspace users. It lives inside Gmail, Docs, Sheets, and Drive. - Claude Thoughtful and accurate. Excels at long-context reasoning, legal, and policy-heavy work. - Grok The social AI. Pulls live data from X (Twitter) and keeps you ahead of real-time trends. - DeepSeek Open-source, cost-efficient, and technically strong, ideal for large-scale or custom AI setups. Here’s the real insight: Mastering AI isn’t about picking one model. It’s about knowing which model to use, when. Tell me in the comments: What’s the most underrated AI model in your opinion? 🔄 Save this guide. Share it with your team. ___________________________________________ 👋 I’m Amit Rawal, Director of AI-led Business Transformation at Google Outside of work, I’m building SuperchargeLife.ai , a global movement to make AI education accessible and human-centered. 🧠 Join my free masterclass: Design Your Life with AI Learn how to work smarter, live longer, and grow richer, with AI as your co-pilot. ♻️ Repost if you believe AI isn’t about replacing us… It’s about retraining us to think better.

A principal recently reached out to tell me how my chapter "Don't Settle for High-Functioning Teams" was eye-opening for her leadership team. The matrix I created has helped many teams assess their collaboration and grow into what I call, a Quadrant 1 High-functioning High-impact team. I remember driving into work about to lead school coaches in our monthly PD about how to best support our teacher-led teams when a question that had been nagging me for years finally made sense. 🤔 How is it that a team can get along and get things done, but still have little to no impact on teacher practice and student learning? Influenced by the words of Jim Knight's 2011 groundbreaking book, Unmistakable Impact, it became clear to me back then that not all team leaders think about "impact". What was even more perplexing to me was: 🤔 🤔 How is it that a team who does NOT get along, who has group conflict and does not have psychological safety CAN have impact on student scores and learning? I needed to create a framework that could explain the relation between how a team functions and its impact. I came up with the “Team Function Impact Matrix”. By viewing teams through 2 lenses (function AND impact) suddenly 4 types of teams emerged and I have made it my life's work to help leaders make sure their team collaboration lands in quadrant 1 so that teachers practice grows and teams reach the outcomes they need for students. I published the first iteration of my matrix in my 2013 bestselling book, The Skillful Team Leader, where I provide indicators for the 4 types of teams and common hurdles facing teacher leaders striving to get to quadrant 1. Since then, I have written more about this dual lens for looking at teams in my 2023 bestselling book, Intentional Moves, which holds nearly 150 strategies for helping your team get to quadrant 1. You can access the chapters for Free on Corwin website: 🎆 Chapter 2 "Don't Settle for High-Functioning Teams" (Intentional Moves. Corwin 2023.) 🎆 Chapter 3 "Alone Together: Overcoming Hurdles to Foster a High-Functioning, High-Impact Collaborative Team" (The Skillful Team Leader. Corwin, 2013.) ...and you can also view a short primer of me explaining my Team Function Impact Matrix on my Channel - Skillful Intentional Team Leadership. (Image below of "Team Function, Impact Matrix" from Intentional Moves: How Skillful Team Leaders Impact Learning. Corwin Press, 2023.) #PsychologicalSafety #Leadership #IntentionalMovesBook #teachers #education | 23 comments on LinkedIn

🛑🟡🟢 Staying in my lane—because this is where the future’s headed. 🚗 A few weeks ago, someone asked me why an English professor is doing so much with AI, and suggested that I “stay in my lane.” Thing is… this is my lane. Composition has always been about digital literacy, critical thinking, and using language to make sense of a changing world. My job is to help students see the road ahead and navigate it with intention. These are the road signs my students are looking at 👇 Employers across industries now expect AI literacy. The road isn’t optional; it’s already here. AI is a tool, not the driver. We want students in the driver’s seat, 🚗 aware of the road ahead and equipped to steer, not passengers blindly staring at their phones with no idea where they’re going. This slide is from an upcoming presentation on ethical AI integration in the classroom, and it captures the why behind what so many of us are doing: preparing students to write, think, and thrive in an AI-shaped world. #AIinEducation #DigitalLiteracy #HigherEducation #TeachingWriting #AIPedagogy #AIIntegration #AIEthics #CriticalThinking #FutureReady #EducationEquity | 10 comments on LinkedIn

📅 Heads-up, K-12 tech leaders: I know budgets are tight and this will not be welcome news, but if you missed it this is a 'need to know' item. Google is rolling out major changes to Google Workspace for Education licensing starting late 2025. The timeline graphic below shows the rollout schedule, and the overview infographic outlines benefits, cost impacts, and action items. ✅ What’s new: unified license types (removing the old “free staff / paid student mix”), new license categories like Gmail-Only and Archived, and more rigid minimum purchase requirements. 💸 What’s changing for your budget: • Education Plus goes from $5 → $6 USD / user / year (global list price) • The old “1 free staff license per 4 paid student licenses” model is sunset; now all active users must have a paid or specialized license • New minimum license quantities will include all enrolled students + staff needing active licenses 🛠 Your next steps (starting now): 1. Audit your current license assignments (who’s active, archived, or Gmail-Only) 2. Run budget scenarios for 2025–2026 under the new pricing 3. Communicate the changes with your leadership / finance team 4. Time renewals or purchase decisions to avoid surprises 📚 For full details, check link in comment👇 **Infographics created with ChatGPT5**

The point isn’t to teach “process over product.” The point is to balance process, product, and progress — so that they are interconnected steps of a larger personal process. Focusing on process highlights the learning and work that goes into creation. Focusing on product highlights the creations themselves. Focusing on progress highlights the student’s growth between products and processes. Often, teaching only looks at products. This marginalizes the other two steps. My personal opinion is that the “process over product” movement is the swinging of the pendulum — a over-compensatory move that (I hope) allows us to reclaim the value of process. But in the end… We’ll need to build models that take all of these into account, so that we’re reclaiming the value of learning while also giving the freedom of choice that product-oriented assessment often allows. ——— Image: Peg Grafwallner’s “Not Yet…and That’s Ok” (2021), which also makes this distinction. It’s definitely a book worth (re)reading in The Age of AI!

Your next hire should be an AI. Using an AI team, you can turn a small company into an enterprise scale operation. There's two ways to set up your AI team in 2025: 1) Build a custom AI Agent with the right components (Model + Memory + Tools) 2) Use specialized AI tools for different business functions From testing 10s of AI tools, I'm keeping an eye on these tools: 1️⃣ AI Agents for General Tasks - Postman (AI/API Agent builder) - DoubleO AI (Agentic Workflows) - LangGraph (AI workers) 2️⃣ Fullstack Engineer - Cursor (Coding) - Replit (Websites) - Lovable (Prototyping) 3️⃣ Knowledge + RAG - Supabase - Redis - Pinecone 4️⃣ Product and Community - ClickUp - Go HighLevel - Slack 5️⃣ Ads & Marketing - ChatGPT - Adcreative AI - Creatify 6️⃣ GTM Engineers - Instantly - Clay - 11x 7️⃣ Workflow Automation - n8n - Make - Zapier 8️⃣ Customer Support - Vectorshift AI - Retell AI - Voiceflow This is what a complete AI team looks like in 2025. Over to you: Any tools that I missed from this AI Team? | 109 comments on LinkedIn

Think chunking is just "split text every 500 tokens"? That's why your RAG system can't find relevant information. It’s time to level up your chunking game 😎 Most developers jump straight to fancy retrieval techniques, but it’s really your chunking strategy that can make or break your RAG performance. So let's break them down from simple to advanced: 𝗦𝗶𝗺𝗽𝗹𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: 1️⃣ 𝗙𝗶𝘅𝗲𝗱-𝗦𝗶𝘇𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Split text into predetermined token/character counts. Super simple to implement but can cut sentences mid-way. Great for prototyping when you need a baseline fast. Would recommend not using in production. 2️⃣ 𝗥𝗲𝗰𝘂𝗿𝘀𝗶𝘃𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Uses prioritized separators (paragraphs → sentences → words) and adapts to document structure. 3️⃣ 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁-𝗕𝗮𝘀𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Leverages format-specific elements like Markdown headers or HTML tags. Great when you have structured documents with clear logical separations. This is usually my default because it respects natural text organization while not being too complex. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗧𝗲𝗰𝗵𝗻𝗶𝗾𝘂𝗲𝘀: 4️⃣ 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Breaks text at meaning boundaries by analyzing sentence embeddings to detect topic changes. Ideal for dense academic papers where semantic boundaries don't align with document structure. 5️⃣ 𝗟𝗟𝗠-𝗕𝗮𝘀𝗲𝗱 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Uses an LLM to identify propositions and create semantically coherent chunks. Most powerful but also most expensive - a good choice for high-value documents where retrieval quality is absolutely essential. 6️⃣ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: An AI agent dynamically decides which chunking strategy to use based on document characteristics. The right approach when you need custom strategies tailored to each document. 7️⃣ 𝗟𝗮𝘁𝗲 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴: Embeds the entire document first, then derives chunk embeddings while preserving full document context. Is a popular technique for technical documents where chunks reference other parts of the document. 𝗧𝗵𝗲 𝗳𝘂𝗻𝗱𝗮𝗺𝗲𝗻𝘁𝗮𝗹 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲 is that your chunks need to be small enough for precise vector search while giving the LLM enough context to generate useful answers, while also not being tooo much context that you overload the content window. 𝗤𝘂𝗶𝗰𝗸 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸: • Prototyping → Fixed-size • Structured docs → Document-based • Dense academic content → Semantic • High-stakes systems → LLM-based or Agentic I would always recommend starting simple and evolving. Learn more in this blog: https://lnkd.in/eYY8c-hN | 17 comments on LinkedIn

🚨 Too many “agent” tools, not enough clarity. I coach SMB teams every week, here’s the cheat sheet I use to pick the right one fast: ✅ Operators, non-technical → Make.com, Flowise ✅ Low-code and self-host → n8n ✅ Build LLM apps + RAG → LangChain + LangGraph, LlamaIndex ✅ Multi-agent teamwork → AutoGen, CrewAI ✅ Quick ship inside ChatGPT → OpenAI Agentic Stack ✅ Enterprise SDK path → Semantic Kernel Save this, share it with your ops lead, and test one small workflow this week. P.S. Which one are you piloting this quarter? Follow Brianna Bentler for practical AI, real SMB wins, and before/after metrics you can copy. Thanks to legendary Greg Coquillo for the amazing graphic! | 119 comments on LinkedIn