IoT Solutions for Industry

What if the most important AI device at the FIFA World Cup isn’t a camera—but the ball itself? ⚽🤖 Modern FIFA match balls contain embedded sensors that capture hundreds of data points every second. Combined with AI-powered computer vision, they help determine the exact moment the ball is played, improving offside decisions, enhancing VAR, and generating rich real-time match analytics. But this is bigger than football. It’s a powerful example of sensor fusion—where edge devices, AI models, and high-performance computing work together to deliver insights in milliseconds. The same principles are transforming industries far beyond sport: 🏭 Smart manufacturing with connected sensors. 🚗 Autonomous vehicles combining cameras, radar, and LiDAR. 🏥 Healthcare devices delivering real-time diagnostics. 🏙️ Smart cities optimizing traffic, energy, and public safety. As AI moves from the cloud to the edge, every connected device becomes a source of intelligence. Turning that data into instant, reliable decisions requires powerful compute infrastructure. This is where AMD is helping drive the next era of AI—from Ryzen AI PCs at the edge, to EPYC processors powering modern data centers, and Instinct accelerators enabling large-scale AI inference and training. The future of AI isn’t just about bigger models. It’s about connecting billions of intelligent devices with the compute needed to make every decision count—in real time. From the football pitch to the factory floor, AI is changing how the world works. #AI #AMD #EdgeAI via @untoldoddities #SportsTech #ComputerVision #SensorFusion #HighPerformanceComputing #DataCenter #DigitalTransformation #Innovation #FIFA

2026 is the year AI leaps off our screens and starts to impact our physical world at scale - unlocking data to inform, advise and empower us to make every operation more efficient, and enable every employee to work smarter. Industrial AI is overcoming friction to acquire data and creating a powerful set of analytics to improve customer economics.   Think of a handheld device on the factory floor that conversationally guides an inexperienced worker through a sophisticated repair using decades of data. Think of the ability to monitor every square foot of space across your network of stores – keeping each one comfortable for customers and employees and making minute-by-minute adjustments to manage power consumption and costs. Think of running a complex operation like a refinery where domain rich AI applications drive plants to perform at optimum level.   The future of AI is in how it connects everything from buildings to plants to refineries, but more importantly how it elevates the people who work with them. Physical AI isn’t just coming; it’s unlocking entirely new possibilities and outcomes across industries. That’s why this tops my #BigIdeas2026 list: I’m confident this is the year the future of physical AI will accelerate.  

AI agents and physical AI are shifting industrial automation from equipment supply to autonomous, self-optimizing systems. The most mature vendors are moving from pilots to production, with robots navigating complex environments and digital twins optimizing the value chain. This CB Insights brief gives a good view of where the top 20 industrial automation companies stand on AI maturity. Three key trends. 1. Leaders like Siemens Industry and ABB are linking AI systems across design, logistics, manufacturing, and maintenance creating compounding benefits. 2. Optimization dominates near-term priorities, while digital twins are emerging as the backbone for connecting hardware and software. 3. Partnerships with tech companies like Microsoft, Google, and Nvidia are essential, but they create new dependencies that must be managed. Siemens at the top of the ranking, combining copilots, edge platforms, and digital twins. Its work with Microsoft and Nvidia expands capabilities but increases reliance on external tech. Honeywell takes a more focused approach, embedding AI into devices and workflows. Its Qualcomm partnership highlights product-level integration over broad system building. ABB advances through its OmniCore platform and acquisitions such as Sevensense and SensorFact, blending robotics, software, and energy management. Schneider Electric pushes AI in energy management, using digital twins and partnerships with Nvidia, Microsoft, and Itron to extend from factory optimization into grid intelligence. The path forward in industrial AI is moving beyond pilots or isolated tools. It will depend on how well vendors embed AI into their platforms, link technologies across domains, and balance the benefits of external partners with the need for strategic independence. Those that will get it right will turn AI from experimentation into durable advantage. Just as critical is how their customers adopt these technologies. Industrial firms must shift from isolated use cases to embedding AI in design, production, energy, and logistics. Success requires not only advanced tools, but also the data, skills, and processes to make AI scale in complex operations.

The next global Industrial AI company may be born in a messy Indian factory. New manufacturing facilities are leapfrogging and deploying state-of-the-art IoT technologies from day one. But the real energy transition opportunity may lie in brownfield manufacturing: facilities that are large in number, highly fragmented, and operationally complex. That is one of the biggest insights I took from our report, India’s Industrial Energy Transition Opportunity, co-authored by TDK Ventures and Theia Ventures. India is one of the world’s most important stress-test markets for industrial technology because it is hard in the right ways: ⚙️ 100+ PLC brands 🗣️ Local language complexity 🏭 Brownfield equipment 🏢 A significant MSME base 💰 Cost-conscious deployment But that is exactly why it can create global winners. India’s IIoT market could grow to nearly $5 billion by 2030. Yet, among the country’s 78 million MSMEs, IIoT penetration remains close to negligible. During our research, the team mapped 124 Indian IIoT startups, and nearly half are still focused primarily on basic data capture from equipment, processes, and facilities. Most factories cannot optimize what they cannot predict. They cannot predict what they cannot measure. And they cannot measure what they have not connected. While many startups are still operating at the “Measure” layer, capturing and contextualizing industrial data, over time they will evolve toward failure forecasting and ultimately resource optimization. In many ways, the foundational data layer for Industrial AI is being built in India right now. The first step is to identify where factory value actually leaks. The report identifies six such areas: 1️⃣ Energy optimization 2️⃣ Predictive maintenance 3️⃣ Process digital twins 4️⃣ Emissions monitoring 5️⃣ Resource and waste optimization 6️⃣ Demand flexibility Across these areas, the most interesting opportunities in India lie in the brownfield factory intelligence stack: edge data capture, retrofit digitization, predictive maintenance, operator copilots, compliance infrastructure, and eventually industrial process AI. The startup that solves India’s industrial complexity can help author the global playbook for economies across Southeast Asia, MENA, Africa, Latin America, and other industrializing markets. Global VCs and CVCs should pay attention. Industrial IoT in India does not only need capital. It also needs global strategic partners, customers, and manufacturing networks to help take proven Indian innovations to the rest of the world. 📖 Read the full report here: https://lnkd.in/g2cE9VJk Thank you to Ravi Jain, Vasan Churchill, Shraya Sapru, and our partners at Theia Ventures for the work behind this report.

The majority of Industrial AI isn’t going into some futuristic, fully autonomous factory. It’s going into: • Catching defects • Keeping lines running • Fixing machines before they break That’s it. Over half the use cases are sitting right there in quality, production, and maintenance. What I found more interesting wasn’t the top of the list… it was the movement. 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 & 𝐑&𝐃 𝐮𝐩 𝐚𝐥𝐦𝐨𝐬𝐭 𝟑𝐱. 😮 AI is starting to show up before anything hits the floor. Not just improving execution… influencing how things are designed, tested, and brought into production. This means different conversations and different people involved. And then there’s the part that made me laugh a bit…“Other” dropped by 70%. 🤣 Fewer side projects. More focus on the parts of the business that run every day. Also worth noting…You don’t see a category here that screams GenAI. Most of this is: • Vision • Time-series data • Operational models The kind of AI that doesn’t demo well… but does show up in results. My biggest takeaway from this chart: Companies are putting AI where: • The problem already hurts • The data already exists • The outcome actually matters to the business Not everywhere. Just where it counts. I wrote a deeper breakdown of what the latest Industrial AI data and trends reveal based on the huge amount of research conducted by IoT Analytics in their 399-page 2025 Industrial AI Report. 𝐅𝐮𝐥𝐥 𝐀𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e2-GJZYJ ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

If you think IoT maturity is about adding more devices, you are missing the real shift. Edge AI and Swarm IoT bring intelligence closer to operations, where systems can coordinate before cloud delay weakens the response. The point is not to connect more objects for the sake of collecting more data. Edge AI and Swarm IoT make IoT more useful when connected systems can understand what is happening near the source and help operations respond sooner. Edge AI supports this shift because some decisions should not wait for every signal to travel back to the cloud. When data is processed near a machine or sensor, teams can act with less delay and less dependence on constant connectivity. Swarm IoT adds coordination. Devices do not only report status; they share signals and adjust behavior as a group. This is where distributed assets start to work more like an operational network than a set of isolated endpoints. The human role remains essential. Leaders need clear governance and safety rules, so local intelligence stays aligned with business goals and does not become uncontrolled automation at the edge. IoT maturity is not measured by the number of connected devices. It is measured by how well intelligence and oversight work together where operations actually happen. #EdgeAI #SwarmIoT

As we close out 2025, I’ve been reflecting on the seismic shifts that defined industry, and what they signal for the future. 2025 was a year of compressed transformation. Persistent volatility in energy prices, supply chains, and labor markets accelerated adoption of IoT, AI, edge computing, and 5G. These technologies are no longer optional, they’re the backbone of modern industrial ecosystems. Analysts confirm this trajectory: 🔹 Deloitte reports that 80% of manufacturing executives plan to allocate 20% or more of their improvement budgets to smart manufacturing initiatives, prioritizing real-time visibility and predictive maintenance.  🔹 McKinsey & Company finds that 88% of companies now use AI in at least one function, but scaling remains a challenge - high performers redesign workflows to unlock growth and innovation.  🔹 Market forecasts show industrial automation growing from $206B in 2024 to $378B by 2030 (10.8% CAGR), driven by Industry 4.0, and AI integration.  🔹 Edge computing is surging too, expected to reach $45B by 2033, enabling low-latency analytics and predictive quality control. What does this mean for our industry? Automation is becoming open, software-defined, and decoupled from proprietary hardware, creating a foundation for adaptability, sustainability, and resilience. AI is moving from pilot projects to embedded intelligence, powering predictive maintenance, autonomous operations, and sustainability gains. At Schneider Electric, we see this every day: open, software-defined automation unlocks innovation through openness, interoperability, and flexibility, enabling manufacturers to scale faster and respond dynamically to market shifts. Looking ahead: AI will not just augment operations, it will redefine competitive advantage. From generative design to autonomous workflows, the next wave of industrial transformation is already here. 👉 What are your reflections on 2025, and where do you see the biggest opportunities in 2026 and beyond?  

𝐁𝐫𝐢𝐝𝐠𝐢𝐧𝐠 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠: 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐢𝐚𝐥 𝐈𝐨𝐓 𝐆𝐚𝐭𝐞𝐰𝐚𝐲𝐬 🌐 The boundary between Information Technology (IT) and Operational Technology (OT) has long hindered holistic industry operations. Industrial IoT gateways are the champions heralding change. ✨ 𝐒𝐧𝐚𝐩𝐬𝐡𝐨𝐭 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: - The IIoT gateway market surged ~14.7% within a year, nearing the $860 million mark, and this trajectory is predicted to continue through 2027. - Major players in this shift are Cisco, Siemens, Advantech, and MOXA. 🏭 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧: IIoT gateways are pivotal in reshaping the manufacturing landscape. By retrofitting even older systems, they facilitate real-time data exchange between operations and IT/cloud realms. This harmonization yields key outcomes: reduced downtimes (as illustrated by Vitesco's preemptive malfunction detection), significant labor cost reductions, and optimized energy use. The result? Streamlined operations, significant savings, and enhanced productivity. 🚀 🛠️ 𝐃𝐞𝐞𝐩 𝐃𝐢𝐯𝐞: 1) 𝑰𝑻/𝑶𝑻 𝑺𝒚𝒏𝒄𝒉𝒓𝒐𝒏𝒊𝒛𝒂𝒕𝒊𝒐𝒏: Legacy equipment, often disconnected, is now plugged into the digital grid. IIoT gateways serve as conduits, ensuring swift, seamless data transitions to IT platforms. 2) 𝑮𝒂𝒕𝒆𝒘𝒂𝒚 𝑭𝒓𝒂𝒎𝒆𝒘𝒐𝒓𝒌𝒔: They're not one-size-fits-all. Four distinct architectures accommodate diverse enterprise needs, ensuring smooth data flows and heightened efficiency. 3) 𝑽𝒆𝒓𝒔𝒂𝒕𝒊𝒍𝒊𝒕𝒚: Modern IIoT gateways juggle multiple roles - from protocol translation to security management, making them indispensable in a robust IIoT ecosystem. 💼 𝐅𝐮𝐫𝐭𝐡𝐞𝐫 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬: 1) 𝑺𝒐𝒇𝒕𝒘𝒂𝒓𝒆 𝑴𝒊𝒈𝒓𝒂𝒕𝒊𝒐𝒏: Companies are transitioning key applications to the cloud, elevating IIoT gateways as primary data traffic controllers. 2) 𝑯𝒂𝒓𝒅𝒘𝒂𝒓𝒆 𝑬𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏: Gateways now sport multi-core processors, AI chipsets, and enhanced security elements, ensuring swifter and safer data processing. 3) 𝑩𝒆𝒏𝒆𝒇𝒊𝒕: IIoT gateways have led to profound IT/OT integrations. Examples include Vitesco Technologies Italy's advanced malfunction prediction and Corpacero's reduced repair costs thanks to predictive maintenance. The once aspirational fusion of IT and OT is now tangible, courtesy of IIoT gateways. The forthcoming industrial epoch? Seamlessly integrated, vastly efficient, and pioneering. 🔍 Source: IoT Analytics (https://lnkd.in/euj3wiUD)

AI at the Edge: Smaller Deployments Delivering Big Results The shift to edge AI is no longer theoretical—it’s happening now, and I’ve seen its power firsthand in industries like retail, manufacturing, and healthcare. Take Lenovo's recent ThinkEdge SE100 announcement at MWC 2025. This 85% smaller, GPU-ready device is a hands-on example of how edge AI is driving significant business value for companies of all sizes, thanks to deployments that are tactical, cost-effective, and scalable. I recently worked with a retail client who needed to solve two major pain points: keeping track of inventory in real time and improving loss prevention at self-checkouts. Rather than relying on heavy, cloud-based solutions, they rolled out an edge AI deployment using a small, rugged inferencing server. Within weeks, they saw massive improvements in inventory accuracy and fewer incidents of loss. By processing data directly on-site, latency was eliminated, and they were making actionable decisions in seconds. This aligns perfectly with what the ThinkEdge SE100 is designed to do: handle AI workloads like object detection, video analytics, and real-time inferencing locally, saving costs and enabling faster, smarter decision-making. The real value of AI at the edge is how it empowers businesses to respond to problems immediately, without relying on expensive or bandwidth-heavy data center models. The rugged, scalable nature of edge solutions like the SE100 also makes them adaptable across industries: Retailers** can power smarter inventory management and loss prevention. Manufacturers** can ensure quality control and monitor production in real time. Healthcare** providers can automate processes and improve efficiency in remote offices. The sustainability of these edge systems also stands out. With lower energy use (<140W even with GPUs equipped) and innovations like recycled materials and smaller packaging, they’re showing how AI can deliver results responsibly while supporting sustainability goals. Edge AI deployments like this aren’t just small innovations—they’re the key to unlocking big value across industries. By keeping data local, reducing latency, and lowering costs, businesses can bring the power of AI directly to where the work actually happens. How do you see edge AI transforming your business? If you’ve stepped into tactical, edge-focused deployments, I’d love to hear about the results you’re seeing. #AI #EdgeComputing #LenovoThinkEdgeSE100 #DigitalTransformation #Innovation

If cloud computing gave us flexibility, edge computing is giving us speed—and that's the real game-changer. As someone who's helped businesses rethink their tech strategy, I see this shift everywhere: from manufacturing to healthcare, the need for real-time decisions is redefining how we process data. Edge computing doesn’t replace the cloud—it complements it. By processing data closer to where it's generated, edge computing cuts latency, improves reliability, and makes true real-time action possible. Here’s how edge is already making an impact: 🚗 Self-Driving Cars → They can’t wait for cloud responses. On-board systems make split-second decisions to ensure safety. 🏭 Smart Factories → Machines detect issues and adjust instantly, avoiding accidents and reducing downtime. ❤️ Healthcare Devices → Wearables and monitors respond in real time, giving doctors live insights that save lives. 🛒 Retail Innovation → AI-powered cameras and sensors adjust digital signage, pricing, or promotions in the moment based on who’s shopping. In other words, edge is where data meets action. Instantly. Pro tip: As companies grow more connected, a hybrid model—cloud + edge—is the future. Use the cloud for storage and heavy analytics, and edge for the urgent, real-time stuff. In my experience, making the right call about where to process data is becoming just as important as what you process. Curious to hear from you: where do you see real-time processing having the biggest impact in your industry? Drop your thoughts in the comments. And if you’re into tech, strategy, and future-ready ideas, follow me for more. #EdgeComputing #CloudComputing #IoT