Manufacturers are investing in AI to improve maintenance, quality, production visibility, asset performance, and decision-making. Most of these use cases pull data from sensors, historians, machines, SCADA systems, MES platforms, engineering workstations, and other operational sources. Some of that data moves to enterprise systems, some to cloud platforms, some to analytics tools or AI models. […]
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I’ve been involved in distributed and embedded computing for about 30 years now, and one of the things I see again and again in the energy sector is how a successful AI pilot fails to become a successful deployment. The model works. The proof of concept proves what it was designed to do. Leadership approves […]
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Most industrial AI systems on the factory floor are limited to monitoring: they read sensor data, populate dashboards, and trigger alerts without ever sending an instruction back to the machine. Coreflux, a company based in Porto, Portugal, co-founded by CEO Hugo Vaz and CTO Paulo Mota, has embedded an AI agent directly inside its MQTT […]
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For decades, the promise of the digital factory was simple: more data equals more efficiency. Yet, as discussed at the ARC Industry Leadership Forum 2026, many manufacturers find themselves trapped in a productivity paradox. Despite having more sensors and dashboards than ever, teams are spending more time reconciling information than building products.To break this cycle, […]
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A predictive maintenance system flags that a motor will fail in three weeks. What happens next usually involves three to four people, spans up to two weeks, and touches the CMMS, ERP, workforce scheduling, and production planning systems before a single wrench turns. Agentic AI compresses that entire coordination chain into roughly 30 seconds. During […]
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Agentic AI will bring thousands, potentially tens of thousands, of AI agents to the factory floor. Every one of them will need data, and not all data. Agents do not perform well when exposed to massive amounts. They need focused, usable data scoped to their task. So, how much should manufacturers let them do without […]
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Software-defined automation moves control logic from dedicated PLCs onto industrial PCs and standard server hardware, while the physical equipment, inputs/outputs, drives, and motors, stays on the shop floor. Engineering changes too, from predefined toolchains with fixed workflows to an open system where teams connect Siemens, third-party, and OEM tools through APIs and a package management […]
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At an IIoT World Energy Day 2026 panel on scalable edge solutions for modern grids, Andrew Foster of IOTech Systems, Cody Falcon of ABB, Brenna Wood of EDF Power Solutions North America, and Janko Isidorovic of Fluence Energy discussed what distributed energy resource fleets require before edge AI can produce useful results. The panel, moderated […]
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Most manufacturers face the same starting point: brownfield operations with equipment from multiple vendors and departments that work toward different objectives. The scope of what needs connecting creates enough resistance that many companies never take the first step. At Hannover Messe 2026, Thomas Roehrl of Siemens described this as the biggest mistake in platform deployment: […]
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At an IIoT World Manufacturing Day panel on data sovereignty and industrial AI, Peter Sorowka of Cybus and leaders from MaibornWolff, SCHUNK, and Schwarz Digits outlined how competitive advantage in manufacturing is forming well before AI reaches full deployment. Many manufacturers are waiting for clearer AI winners, the right platform, the right models, the right […]
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ABB resolves 80% of measurement device faults through an AI copilot, without a support call. HighByte compressed Alcon’s one-year plant-wide digital transformation to less than one month. InfluxData and Litmus take manufacturers from three sites to 300, with positive ROI in 60 to 90 days. Cybus reports 9% less downtime and 23% lower cloud costs […]
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During the “Edge AI: Driving Smarter Machine Health Monitoring for Energy Infrastructure” panel at IIoT World Energy Day 2026, poll results showed that most energy operators are just beginning to explore AI for asset monitoring. Some have completed a first deployment, but only a small percentage have scaled these solutions across multiple assets or sites. […]
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