Why AI Vision is hard to sell despite its benefits

Sometimes, in order to build innovative technology products, we need to step back from the benefits of the core technology and the solution it generates. We need to look at the unique risks that these technologies may generate. Risks that are not captured in standard frameworks. Not just for mitigation purposes. Sometimes, product differentiation opportunities can be found in the approach to addressing unique risks. This paper highlights how sensors create unique risks that are often overlooked in AI governance frameworks (like NIST’s AI RMF or the EU AI Act). These risks stem from the materiality of sensors (their physical properties, deployment, and embedded intelligence) and from calculative models (economic and social logics driving their proliferation). The paper proposes a sensor-sensitive AI risk diagnostic framework with five dimensions: Calibration: Miscalibrated sensors (e.g., cameras not tuned for darker skin tones) can produce biased or unsafe outputs. Documentation: Lack of adequate datasheets/model cards obscures transparency and accountability. Proprietary Data Profusion: Economic models incentivize massive, opaque data collection, often of poor quality. Privacy: Tiny, pervasive sensors enable hidden data capture and inference risks; notice/consent mechanisms break down. Waste: Environmental harms stem from manufacturing, rare-earth extraction, batteries, and “stranded e-waste” in remote deployments. The paper also has some rich generic reading material on sensors, for those interested 😌 #ai #artificialintelligence #sensors #iot #tinyml

Sometimes, in order to build innovative technology products, we need to step back from the benefits of the core technology and the solution it generates. We need to look at the unique risks that these technologies may generate. Risks that are not captured in standard frameworks. Not just for mitigation purposes. Sometimes, product differentiation opportunities can be found in the approach to addressing unique risks. This paper highlights how sensors create unique risks that are often overlooked in AI governance frameworks (like NIST’s AI RMF or the EU AI Act). These risks stem from the materiality of sensors (their physical properties, deployment, and embedded intelligence) and from calculative models (economic and social logics driving their proliferation). The paper proposes a sensor-sensitive AI risk diagnostic framework with five dimensions: Calibration: Miscalibrated sensors (e.g., cameras not tuned for darker skin tones) can produce biased or unsafe outputs. Documentation: Lack of adequate datasheets/model cards obscures transparency and accountability. Proprietary Data Profusion: Economic models incentivize massive, opaque data collection, often of poor quality. Privacy: Tiny, pervasive sensors enable hidden data capture and inference risks; notice/consent mechanisms break down. Waste: Environmental harms stem from manufacturing, rare-earth extraction, batteries, and “stranded e-waste” in remote deployments. The paper also has some rich generic reading material on sensors, for those interested 😌 #ai #artificialintelligence #sensors #iot #tinyml

𝗛𝗼𝘄 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗮𝗶𝗻𝘁𝗲𝗻𝗮𝗻𝗰𝗲 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘀 𝟭𝟬𝘅 𝗥𝗢𝗜 Walk through a modern factory floor and everything looks calm. Machines hum, production flows, operators stay focused. But beneath that surface, a quiet revolution is underway. 🔴 The old way → wait for machines to break, then scramble to fix them. 🟢 The smart way → know exactly when maintenance is needed before anything goes wrong. Here’s how predictive maintenance (PdM) transforms operations in 6 steps 1️⃣ 𝗖𝗼𝗻𝗻𝗲𝗰𝘁𝗲𝗱 𝗠𝗮𝗰𝗵𝗶𝗻𝗲𝘀 – smart sensors track vibration, temperature & pressure in real time. 2️⃣ 𝗦𝗲𝗮𝗺𝗹𝗲𝘀𝘀 𝗗𝗮𝘁𝗮 𝗙𝗹𝗼𝘄 – IoT networks stream data from every asset to centralized platforms. 3️⃣ 𝗥𝗲𝗺𝗼𝘁𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 – dashboards provide instant visibility into health & performance. 4️⃣ 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 – AI/ML forecast failures weeks or months in advance. 5️⃣ 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗔𝗹𝗲𝗿𝘁𝘀 – teams get early warnings with enough lead time to plan repairs. 6️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗪𝗼𝗿𝗸 𝗢𝗿𝗱𝗲𝗿𝘀 – systems schedule technicians, order parts & optimize workflows. The impact is measurable: • 35–50% less unplanned downtime( US Department of Energy) • 5–10% lower maintenance costs(Deloitte Research) • 20–40% longer equipment life (Nucleus Research) • 70–75% fewer breakdowns(Nucleus Research) • Potential for 10x ROI ( US Department of Energy) This isn’t future tech. It’s happening today , in factories, energy plants, and logistics hubs worldwide. Companies embracing PdM aren’t just cutting costs; they’re building resilience and competitive advantage. The real shift isn’t just technological. It’s cultural. From firefighting to foresight. From breakdowns to precision. From cost to strategy. #PredictiveMaintenance #Industry40 #SmartFactories #Sustainability #AI Ref: Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 - Zeki Murat Çınar et all.

🚀 𝐒𝐄𝐂𝐎 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐧𝐞𝐰 𝐀𝐈 𝐀𝐩𝐩𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐀𝐩𝐩 𝐇𝐮𝐛, 𝐧𝐨𝐰 𝐰𝐢𝐭𝐡 𝐟𝐮𝐥𝐥 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐚 𝐭𝐫𝐢𝐚𝐥 𝐨𝐟 𝐂𝐥𝐞𝐚 🧩🧠 Read the full PR here: 👉 https://lnkd.in/dDFqqYAX SECO has launched seven new #AI solutions in the past month, each one rigorously validated within SECO’s #hardware ecosystem to ensure seamless #integration and #deployment. This release also introduces: 🔹 A new “#Deploy” button for instant access to a 90-day Clea trial 🔹 Full #documentation available in the #SECODeveloperCenter, with step-by-step #guides and #specifications 🧩🧠 The new #applications span diverse use cases – from industrial #QualityControl and predictive #analytics to on-device #ConversationalAI – enabling #clients to focus on what truly differentiates their #products while SECO handles the complexities of #AI. ✨ 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗮𝗽𝗽𝘀 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: 🔹 #Llama – Local Chatbot with Intel Corporation® #OpenVINO™ 🔹 #SensorForecast – Smart Building (NeuralProphet) 🔹 #FaceID + Edge Impulse 🔹 #SceneTextDetection – High Accuracy & Light Model 🔹 #PCB Defect Detection 🔹 #YAMNet – Training Experience 💬 “We are enabling our clients to build powerful, custom #AI/ML #pipelines without getting bogged down by the underlying technological complexities,” says Fausto Di Segni, Head of IoT and AI at SECO. Ready to transform your #product with #AI? Start here with SECO Application Hub 👉https://apphub.seco.com/ #SECOBusiness #SECOAppHub #Clea #AI #EdgeComputing #QualityControl #PredictiveAnalytics #ConversationalAI

🚀 𝐒𝐄𝐂𝐎 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐧𝐞𝐰 𝐀𝐈 𝐀𝐩𝐩𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐀𝐩𝐩 𝐇𝐮𝐛 – 𝐧𝐨𝐰 𝐰𝐢𝐭𝐡 𝐟𝐮𝐥𝐥 𝐝𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 𝐚𝐧𝐝 𝐚 𝟗𝟎-𝐝𝐚𝐲 𝐂𝐥𝐞𝐚 𝐭𝐫𝐢𝐚𝐥 🧩🧠 With this release, #Clea takes center stage as the enabler of on-device #AI. Through the new “#Deploy” button, you can instantly activate a 90-day free trial of #Clea and start experimenting with validated #AI solutions across SECO’s #hardware ecosystem. 🔹 Full #documentation, guides, and specs are now available in the #SECODeveloperCenter – making it easier than ever to deploy, customize, and scale your AI pipelines with Clea. 🔹 From #QualityControl and #PredictiveAnalytics to #ConversationalAI, Clea empowers you to innovate while seamlessly managing deployment and integration. ✨ Discover all the new apps now available! 👉 Start your Clea journey today on the SECO Application Hub: https://apphub.seco.com/ #Clea #SECOAppHub #AI #EdgeComputing #IoT #Innovation #MachineLearning

𝐀𝐈 𝐈𝐬𝐧’𝐭 𝐒𝐜𝐢-𝐅𝐢 𝐀𝐧𝐲𝐦𝐨𝐫𝐞: 𝐈𝐦𝐚𝐠𝐞𝐆𝐫𝐚𝐟𝐢𝐱’𝐬 𝐏𝐥𝐚𝐲𝐛𝐨𝐨𝐤 𝐟𝐨𝐫 𝐂𝐨𝐧𝐧𝐞𝐜𝐭𝐞𝐝 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 & 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬 For years, AI sounded like something out of a sci-fi script. But in boardrooms of manufacturers and chemical plants, the conversation has shifted from “𝑊ℎ𝑎𝑡 𝑖𝑠 𝑖𝑡?” to “𝐻𝑜𝑤 𝑠𝑜𝑜𝑛 𝑐𝑎𝑛 𝑤𝑒 𝑚𝑜𝑛𝑒𝑡𝑖𝑧𝑒 𝑖𝑡?" At #ImageGrafix, our job was to turn complex #IIoT jargon—#sensors, #digitaltwins, #predictiveanalytics— into stories that decision-makers could relate to. - We showed how a pump failure predicted by a sensor saved millions in unplanned downtime. - We reframed “Digital Twins” from a buzzword into a boardroom tool for CAPEX protection and ESG compliance. - We made AI about resilience and ROI, not robots and hype. How we told the story mattered as much as the technology itself The takeaway? AI in maintenance isn’t sci-fi...it’s survival. And the right Company doesn’t just sell software; 𝐢𝐭 𝐬𝐞𝐥𝐥𝐬 𝐜𝐞𝐫𝐭𝐚𝐢𝐧𝐭𝐲 𝐢𝐧 𝐚 𝐯𝐨𝐥𝐚𝐭𝐢𝐥𝐞 𝐰𝐨𝐫𝐥𝐝. ImageGrafix Asset Management and IOT Solutions Hexagon Asset Lifecycle Intelligence Gaurav Singh Deepak Sethia Ashwini T Spandan Basu ↗️ #ImageGrafix #Hexagon #KingdomofSaudiArabia #AI #DigitalTwins #ConnectedMaintenance #IIoT #IndustrialSoftware

🚀 𝐓𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐜𝐞: 𝐋𝐞𝐚𝐧 𝐒𝐢𝐱 𝐒𝐢𝐠𝐦𝐚 𝟒.𝟎 𝐢𝐬 𝐇𝐞𝐫𝐞 The manufacturing landscape is experiencing a revolutionary transformation. 𝐋𝐞𝐚𝐧 𝐒𝐢𝐱 𝐒𝐢𝐠𝐦𝐚 𝟒.𝟎 (𝐋𝐒𝐒 𝟒.𝟎) represents the next evolution of continuous improvement, seamlessly integrating traditional DMAIC methodology with cutting-edge Industry 4.0 technologies. What makes LSS 4.0 a game-changer? 🔹 𝐈𝐨𝐓-𝐃𝐫𝐢𝐯𝐞𝐧 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠: Real-time sensor data replaces manual measurements, providing continuous visibility into every aspect of production 🔹 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬: Machine learning algorithms predict quality issues and equipment failures before they occur, shifting from reactive to proactive optimization 🔹 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧 𝐒𝐢𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧𝐬: Virtual replicas of production processes enable risk-free testing of improvements, reducing implementation cycles from weeks to days 🔹 𝐀𝐮𝐭𝐨𝐧𝐨𝐦𝐨𝐮𝐬 𝐏𝐫𝐨𝐜𝐞𝐬𝐬 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Systems self-adjust parameters based on real-time data, continuously improving efficiency without human intervention 𝐓𝐡𝐞 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐢𝐬 𝐩𝐫𝐨𝐟𝐨𝐮𝐧𝐝: ✅ Traditional DMAIC → Data-driven DMAIC 4.0 ✅ Reactive control → Predictive intelligence ✅ Manual analysis → AI-augmented decision-making ✅ Periodic improvements → Continuous autonomous optimization Research shows that organizations implementing LSS 4.0 achieve up to 27% improvement in Overall Equipment Effectiveness while reducing quality variations by 64%. The framework doesn't just eliminate waste—it prevents it from occurring in the first place. 𝐓𝐡𝐞 𝐛𝐨𝐭𝐭𝐨𝐦 𝐥𝐢𝐧𝐞: LSS 4.0 is transforming manufacturing from reactive problem-solving to intelligent, self-optimizing systems that maximize productivity, quality, and sustainability simultaneously. Are you seeing these technologies reshape quality management in your industry? What's your experience with integrating AI and IoT into traditional Lean practices? #LeanSixSigma #Industry40 #SmartManufacturing #DigitalTransformation #ContinuousImprovement #IoT #ArtificialIntelligence #DigitalTwins #ManufacturingExcellence #DMAIC #PredictiveAnalytics #QualityManagement #OperationalExcellence #Manufacturing #Automation

🚨💼 When top experts leave, their knowledge often goes with them ...  How can companies keep this expertise while stabilizing complex production processes? Skills shortage, knowledge loss, and process complexity – 3️⃣ major challenges in mechanical and plant engineering. 👉🏼 Example: In plastics processing, a temperature error can cause hundreds of meters of scrap. Traditional methods fail to detect and analyze such process deviations early enough. AI assistance systems combine machine learning with expert know-how – creating robust hybrid solutions. ✅ Secure and share expert knowledge across the team ✅ Increase process stability and detect errors early ✅ Enable data-driven optimization and new digital services 💡 For machine and plant builders, this means fewer unplanned downtimes, reduced scrap, and a stronger service business. IoT-driven AI assistance systems also open the door to new digital offerings – from predictive maintenance to “as-a-Service” models. 👉 Explore how XITASO develops tailored systems like these – from use case identification to system architecture and IoT platform integration: 🇺🇸/🇬🇧: https://lnkd.in/em7teKNG 🇩🇪:  https://lnkd.in/ejschcrR #AIassistancesystem #AI #Industry40 Michael Buchenberg Kathrin Mönch Ulrich Huggenberger

Building the Future of Industrial Safety with Edge AI Safety isn't a suggestion—it's a system. We’ve leveraged Edge AI to build an autonomous safety platform that sees, understands, and acts in real time. Our solution ensures zero-latency safety compliance and protects your most valuable assets, all without sending a single video stream to the cloud. 🧠 At Akhila Labs, our AI-Powered Industrial Safety & Surveillance platform is a testament to what's possible with intelligent hardware and software co-design. Here's how we engineered this solution: Hardware Acceleration: We used a custom embedded board with an NPU to offload intensive AI tasks, ensuring high performance on a low-power budget (sub-2W). 💡 Optimized AI: Our model, an optimized Yolov5s, is enhanced with 8-bit integer quantization, drastically reducing its size and boosting inference speed for real-time detection. ⚙️ Privacy by Design: The system processes video locally on the device, sending only minimal metadata to a dashboard, protecting sensitive data. 🔐 By solving the critical challenge of achieving real-time object detection on a small power budget, we are enabling a new standard for industrial safety. Discover how we're building the future of industrial safety. 👉 Ready to discuss how Edge AI can transform your industrial operations and safety protocols? Let's connect and build something safer, smarter, and more efficient. #EdgeAI #IndustrialSafety #AI #ComputerVision #EmbeddedSystems #IoT #AkhilaLabs #SafetyFirst

Vision AI is becoming the nervous system of manufacturing sites. It’s no longer about a single camera spotting a scratch or a sensor flagging a vibration. Sites are ecosystems: suppliers, inbound, plants, logistics, people , all moving parts of the same story. Vision AI is now stepping up as the context engine that stitches these fragments into actionable narratives. How It Works Suppliers & Logistics feed material quality, inbound checks, and outbound movement. Plants bring together cameras, IoT sensors, and operator logs across multiple lines. Context Engines connect the dots → temporal & multi-line context, multi-modal fusion of video, sensors, and logs. Ops & People translate those insights into root cause analysis, supplier traceability, site-wide action loops, and quality/ops alerts. Governance ensures every decision is explainable with drift monitors, data contracts, SLA dashboards, and audit trails. Why Its important : Defect slippage is reduced before products leave the warehouse. Downtime minimized by catching systemic issues across lines. Compliance & audit made provable with contextual evidence. Throughput increased by closing the loop between suppliers, machines, and people. My take: Vision AI is no longer about isolated flagging. It’s becoming the operational nervous system for manufacturing sites , linking suppliers, plants, logistics, and people into one continuous loop of context, action, and accountability. Whats your view ? There are other aspects like digital twin, which I will pick in the coming week. #VisionAI #ManufacturingAI #EnterpriseAI #IndustrialAI #artificialintelligence