AI Drives Industrial Growth and Data Infrastructure

The objective isn’t perfect data. It’s reduced friction. 💯 That’s the key insight from our latest Viewpoint by Troy Mahr as we wrap up his three-part series on scaling insight without disrupting production and addressing OT technical debt. One-time cleanup efforts fade because plants change: sensors get swapped, lines get modified, new products get introduced. Without a way to preserve meaning through change, the same debt quietly rebuilds. The fix isn’t a documentation project. It’s a practical roadmap built on three core pillars. Read the article to learn more: https://bit.ly/4dkWRGX #industrialdatamanagement #smartmanufacturing

**Static traffic signals are a relic—let’s retire them.** Fixed‑interval timers are the legacy firmware of a bygone era. They cycle green, yellow and red on a clock, oblivious to demand, and cost us fuel, time and emissions. Our Phoenix pilot proved the alternative: swapping a 60‑second fixed cycle for a 5 ms AI‑driven algorithm cut average queue length 42 % and stop‑and‑go emissions 18 %, using the same hardware. The difference is the firmware. **Semiconductor‑first traffic strategy** 1. **Edge‑compute ASICs** at every intersection to process video, radar and V2X data locally. 2. **Dynamic phase scheduling** that re‑optimizes every few seconds. 3. **Closed‑loop feedback** from downstream flow to continuously refine timing. 4. **Secure OTA updates** to evolve logic without costly field swaps. Clinging to timer‑based signals throttles autonomous‑vehicle coordination, emergency‑vehicle preemption and the smart‑city data fabric. It’s a 4G network trying to run a 5G world. **Takeaway:** Intelligent mobility starts with a chip, not a clock. Retire the timer relic and make real‑time, semiconductor‑driven orchestration the new standard for congestion‑free cities.

The interaction between digital systems and reality is a permanent headache. Which data, with which sensors, and how much of the picture do we really get? Turns out, in practice, delivery optimization can be less about models than shovels. (picture: Aminata)

Edge AI in automotive is not a future concept. It is already here. In this Automotive Edge AI: Virtual Sensors session, we show how our NXP eIQ Auto Machine Learning SDK brings real-time intelligence to the S32K566. See how developers can deploy ML directly on automotive-grade MCUs and unlock new levels of insight without added hardware complexity. We walk through the full workflow from model preparation to deployment, and show how virtual sensing and predictive analytics can be applied to real-world automotive systems. Here is what you will learn: ⚙️ How the eIQ Auto SDK compiles and optimizes ML models for the S32K automotive MCU family 📉 How quantization reduces model size while maintaining efficient, accurate inference on the S32K566 🚗 How real-time ML workloads run directly on S32K5 devices to enable software-defined sensing 🔍 How AI-based virtual sensors reduce cost and expand system-level visibility Whether you are working on BMS, thermal management, zonal architectures or other safety-critical systems, this session shows what is possible today. 🎥 Watch the video: https://okt.to/SLXEfI

Exciting times for the automotive industry as Edge AI transforms how we gather insights! The integration of NXP's eIQ Auto SDK on S32K566 is a game-changer for real-time intelligence. How do you see this impacting future automotive innovations?

6, but the future of smart mobility real competition is not: "Who's the cheaper car?" Instead: Who formed it: - User ecosystem - Service system - Intelligent - Community - Data closure The future: Mobility will increasingly look like: "Smart terminals." VII. Energy This is the most underestimated direction of the future. Because: AI, Robotics, Mobility. Finally it all comes back: Energy. Includes: - Battery - Energy Storage - Outdoor Energy - Charge - Energy management The future: The United States will pay more and more attention to: - Home Energy Storage - Outdoor Energy - Backup power - Solar integration Why do these directions converge? The future: AI Hardware + Robotics + Mobility + Energy In fact, it will gradually merge into: "The intelligent physical world." For example: Future eBike: Possible: - AI Navigation - Automatic diagnosis - OTA - Battery Management - User Data - Community networking Robots of the future: The essence is also: - Battery - Electric motors - Sensors - AI - OTA - Service

Automation moves things. Intelligence moves them in the right direction. At NVIDIA GTC, the message was clear: the future of industrial operations isn't just more robots — it's smarter systems. Simulation before deployment. AI agents that adapt in real time. Supply chains that think. SoftServe is building exactly that and being named NVIDIA's Partner of the Year for Energy, Power & Utilities tells us we're on the right track. 🏆

You don't need to replace your 15-year-old machine. You need to give it a brain. www.reiiautomation.com Scrapping perfectly functional industrial equipment just because it lacks a digital dashboard is one of the most expensive mistakes in manufacturing. REII retrofits your existing machinery with proprietary sensors and Edge AI turning legacy assets into smart, connected equipment in hours, not months. No new machine purchase. No complex rewiring. No production downtime during installation. Your old equipment already knows when it's about to fail. Now it can finally tell you. Which machine in your facility deserves a second life? #LegacyEquipment #IndustrialRetrofit #REIIAutomation #EdgeAI #SmartFactory #ManufacturingIndonesia

Join us for a FREE webinar! AI in the industrial space is moving fast - but what really works? We’ll cover where AI is working today, why many pilots fail, and what it takes to move from a demo to a reliable shop-floor system. The discussion will focus on real constraints: data quality, cameras and sensors, edge hardware, PLC/MES integration, operator workflow, maintenance, and cost. The goal is to help manufacturers identify good AI use cases, avoid weak projects, and build systems that can actually run in production.   Hosted by: German Suvorov and Kristen Quasey of Siemens Register here: https://lnkd.in/e29BfQiG

We are solving the connectivity issue now with all fiber build outs. We are actively solving the powering bottleneck. The next constraint is the home itself. Robots and AI need low latency, local compute, and structured infrastructure inside the house. The future home will be a mini data center. The companies that build across all layers will win. "Let's see the forest through the trees".