How AI can enhance design engineering with human judgment

Think of AI as your engineering copilot - not a substitute. From generative design that explores millions of possibilities, to digital twins and predictive analytics that optimize performance before a prototype is even built. AI is transforming how engineers create, test, and deliver solutions.              Discover how AI is reshaping the engineering landscape, the new skills engineers need, and why companies are racing to build AI-ready teams.   Read the full blog: https://bit.ly/3JmQpmx #Zobility #ArtificialIntelligence #EngineeringInnovation #AIDrivenEngineering #GenerativeDesign #DigitalTwins #PredictiveAnalytics #FutureOfEngineering #InnovationInAction #TechTransformation #AIRevolution

AI in Engineering: A Transformation, Not a Replacement Artificial Intelligence is reshaping engineering—but not replacing engineers. AI is a powerful tool, while core skills like critical thinking, problem-solving, and domain expertise remain essential. 🔹 AI in Engineering Isn’t New Engineers have used AI for decades to optimise designs, analyse vast datasets, strengthen decision-making, and build intelligent control systems. 🔹 From Traditional AI to GenAI Traditional AI focused on predictions and classifications. GenAI now brings the power to generate novel outputs. 🔹 What AI Will Do for Engineers • Speed up design • Automate repetitive calculations • Analyse large datasets (rainfall, traffic, environmental impact) • Predict failures • Improve efficiency and accuracy AI will redesign the toolkit of engineers, but it will never replace the engineer behind the toolkit. #ReshapeEngineering #AIEngineering #TraditionalAI #GenAI #CivilEngineering #Innovation #FutureOfEngineering #TechTools #EngineeringExcellence

𝗛𝗼𝘄 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗜𝘀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗠𝗮𝗻𝘂𝗳𝗮𝗰𝘁𝘂𝗿𝗶𝗻𝗴 New research published on November 14, 2025 shows that generative AI is helping manufacturers design better products, cut waste, and improve efficiency on the shop floor. Generative AI lets engineers explore thousands of design options at once, which shortens development timelines and reduces material waste by up to ten percent. On the production side, AI powered insights are helping companies improve cycle times by twenty to thirty percent and reduce errors by twenty five percent. Industry adoption is growing fast. Seventy seven percent of manufacturers now use AI for tasks like quality control, inventory management, and production optimization. New tools from companies like Rockwell Automation, Siemens, and NVIDIA are accelerating these changes even more. The research also makes one thing clear. Human judgment still matters. AI expands what people can do, and the best results come from a partnership between human creativity and machine intelligence. Read the full article here: https://lnkd.in/eScja7c7 For more AI and Workforce updates, follow Dan's Career Corner. #AI #DansCareerCorner #FutureOfWork #SmartFactories #CareerIntelWithDan

Before We Talk About Replacing Engineers with AI, Let’s Talk About Empowering Them AI is rapidly transforming the technology landscape, and it's tempting to jump straight into conversations about automation and replacement. But I believe the real opportunity lies in augmentation first. Before we consider replacing engineering teams, we should focus on how AI can enhance them—helping us build smarter solutions, accelerate development cycles, and reduce cognitive load. AI should be a tool that amplifies human creativity and problem-solving, not one that sidelines it. Let’s invest time in learning how to leverage AI effectively—integrating it into our workflows, decision-making, and product strategies. The future of engineering isn’t about humans vs. machines. It’s about humans with machines. Curious to hear how others are approaching this balance in their teams. How are you using AI to empower your engineers? #AI #EngineeringLeadership #TechThoughts #FutureOfWork #HumanCenteredAI

AI no longer lives just in servers 🧠 It’s stepping into the real world. 🤖 Embodied systems and agentic AI are the next frontier. We’ve talked about large models, reasoning capabilities, and modular architectures. But here’s what’s coming: AI with a body, senses, and agency. Agents that “see” a problem, “move” to solve it, “adapt” to the physical world. Take a few signals: - Gemini Robotics 1.5 lets robots perceive, plan, and act in new environments without domain-specific training. - Embodied AI isn’t just novelty any more; it’s research-ready, with surveys showing how world models, multi-modal sensors, simulation ↔ real-world transfer, and embodied agents are maturing. Why this matters for you (AI engineers, researchers, creators): - If you’re only building for text or vision, you’re cutting off half the world. Embodied systems add motion, touch, context, and real-world feedback. - Your learning path: once you’ve mastered model size + reasoning + architecture, the next layer is interaction + environment + physical grounding. - For your brand message (progress · play · purpose · peace), this is the play piece: building AI that moves and does, not just talks. - For product/design: Deployment of embodied AI introduces new challenges (hardware, sensors, ethics, safety, adaptation); mastering that gives you an edge. Question for you: What domain do you think embodied AI will hit first in scale and impact: manufacturing, healthcare, home automation… or something totally unexpected? 👇 #AI #EmbodiedIntelligence #AgenticAI

Redefining Quality: When AI Becomes the Sharpest Eye in the Factory Quality control used to rely on human intuition , but that era is ending. With AI, Machine Learning, and Deep Vision systems, companies can now detect what the human eye can’t. From micro-defects on metal surfaces to subtle inconsistencies in leather or solar panels, intelligent inspection is transforming how we define “quality.” In R&D departments, this shift means more than automation , it’s predictive intelligence. Machines are not just checking for defects; they’re learning how to prevent them. AI isn’t replacing experience. It’s amplifying precision, accelerating innovation, and creating a future where every product tells a story of perfection. Are we ready to trust the machine’s eye more than our own? 👁️ #ArtificialIntelligence #MachineLearning #DeepLearning #QualityControl #SmartManufacturing #ComputerVision #Industry40 #Innovation #RAndD #Automation #AIInIndustry

🤖 The Next Leap in Computer Vision: Turning Pixels Into Business Power Just a few years ago, computer vision was about recognizing cats and cars in images. Now? It’s recognizing opportunities. 🚀 Thanks to breakthroughs in deep learning, transformer-based vision models, and multimodal AI (like combining images + text), machines can now see, understand, and predict with human-level accuracy — sometimes even better. Here’s where it gets exciting for businesses 👇 🏭 Manufacturing: Vision AI detects defects faster than any human eye — reducing waste and saving millions. 🛒 Retail: Smart cameras track product placement and customer behavior to optimize layouts in real time. 🚗 Transportation: Advanced object detection improves road safety and enables autonomous logistics. 🏥 Healthcare: AI models analyze medical images to support early diagnosis and precision treatment. But the real magic lies in data-driven decisions. When machines can interpret visual data instantly, companies can move from reactive to proactive — predicting issues before they happen. The question is no longer “Can computer vision work?” It’s “How fast can we adopt it to stay ahead?” 👀 How do you see computer vision reshaping your industry? #ArtificialIntelligence #ComputerVision #DeepLearning #Innovation #BusinessTransformation #AIinBusiness #Technology

🧠 Myth: AI will replace engineers. Reality: AI will empower engineers — automating repetitive work so they can focus on creativity and problem-solving. The future isn’t human or machine. It’s human + machine. 💡 Follow Stork Technologies for more smart insights on how technology is transforming engineering. #Automation #Engineering #Manufacturing #FutureOfWork #AI

🤖 𝐌𝐞𝐞𝐭 𝐅𝐢𝐠𝐮𝐫𝐞 𝟎𝟑 — 𝐓𝐡𝐞 𝐇𝐮𝐦𝐚𝐧𝐨𝐢𝐝 𝐑𝐨𝐛𝐨𝐭 𝐒𝐡𝐚𝐩𝐢𝐧𝐠 𝐭𝐡𝐞 𝐍𝐞𝐱𝐭 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐒𝐡𝐢𝐟𝐭 Figure AI’s latest creation, Figure 03, isn’t just another robot — it’s a glimpse into the next phase of human-robot collaboration. At launch, it can perform simple domestic tasks — folding clothes, loading a dishwasher — still under human supervision. But the ambition runs much deeper. Through its Helix neural network, Figure AI is building a continuously learning system — one that adapts, refines, and scales to handle more complex, real-world tasks across industries. This isn’t just automation — it’s embodied intelligence. Machines that can perceive, reason, and act — not just compute. 💡 As humanoid systems evolve, enterprises will need new talent, architecture, and governance models to integrate them safely and effectively. That’s where organizations like 𝐘𝐀𝐋𝐋𝐎 𝐆𝐫𝐨𝐮𝐩 step in — helping leaders build the frameworks, teams, and AI roadmaps to prepare for this human + machine era. 📢 The question isn’t if humanoid AI will enter the workforce — it’s how ready your enterprise will be when it does. #FigureAI #Helix #HumanoidAI #FutureOfWork #DigitalTransformation #YALLOGroup

#Foundations of #Prompt #Engineering The fundamental tenets of prompt engineering are these foundational principles that ensure you get the best possible results from an AI model. Give clear direction. Be specific about what you want the AI to do, defining the objective, style, and scope of the task. For example, instead of asking for a "tagline," specify: "Create a catchy and eco-friendly tagline emphasizing the natural and sustainable aspects of a new skincare line." Provide sufficient context. Offer relevant background information to help the AI understand the scenario. Context helps the model align its output with your vision and ensures a more accurate response. Specify the format of the output. Tell the AI how you want the answer structured, whether as a list, a table, a JSON object, or a summary. This saves time and minimizes the need for post-processing. Use examples. Guide the AI's response by including examples of the desired input-output behavior. This is particularly useful for controlling the tone, style, or specific formatting of the output. Iterate and refine. Prompt engineering is an iterative process. Analyze the AI's initial response, then refine your prompt to improve the accuracy and relevance of subsequent outputs.