Integrating AI With IoT Devices

The power sector is changing fast, and AI is at the center of this transformation. From predicting outages before they happen to improving energy distribution, AI is making electricity more reliable, efficient, and sustainable. But how exactly is AI reshaping the industry? 1. Predicting failures before they happen. Power outages can be costly and disruptive. AI-powered predictive maintenance helps utilities identify potential failures in transformers, power lines, and substations before they occur. By analyzing data from sensors and historical trends, AI reduces downtime and ensures a more stable power supply. 2. Smarter energy distribution. Electricity demand fluctuates throughout the day. AI helps balance supply and demand in real time, ensuring power is distributed where it’s needed most. This minimizes waste, lowers costs, and improves overall grid efficiency. 3. Optimizing renewable energy. Renewable energy sources like solar and wind are unpredictable. AI helps by analyzing weather patterns and adjusting energy production accordingly. This means more stable integration of renewables into the grid. While AI is transforming the power sector, technology alone isn’t enough. The biggest challenge is adoption. Getting companies, governments, and individuals to embrace these changes. For digital transformation to succeed, the industry needs: → Skilled talent → Better infrastructure → And a willingness to rethink traditional ways of managing power AI is here to stay, and its impact on energy is growing. The question is: Are we ready to maximize its potential?

The energy grid is under immense strain from extreme weather, wildfires, and rising electricity demand. As these pressures increase, so does the need for smarter, more resilient and reliable energy grids.   Utilidata, a company that is part of Microsoft's Climate Innovation Fund portfolio, is redefining energy delivery through its AI platform, Karman. This technology empowers utilities to optimize energy delivery and make better decisions about how to manage the grid by, for example, storing electricity in batteries during off-peak hours and distributing it when it's needed. As a result, electric vehicles and solar panels become flexible, valuable assets that help meet grid demand.   Embedding AI directly into the grid infrastructure helps utility decision-makers make more informed decisions and better serve customers. This innovation highlights the power of AI to modernize critical infrastructure and transform the energy sector.

From Blueprint to Battlefield: Reinventing Enterprise Architecture for Smart Manufacturing Agility
   Core Principle: Transition from a static, process-centric EA to a cognitive, data-driven, and ecosystem-integrated architecture that enables autonomous decision-making, hyper-agility, and self-optimizing production systems.   To support a future-ready manufacturing model, the EA must evolve across 10 foundational shifts — from static control to dynamic orchestration.   Step 1: Embed “AI-First” Design in Architecture Action: - Replace siloed automation with AI agents that orchestrate workflows across IT, OT, and supply chains. - Example: A semiconductor fab replaced PLC-based logic with AI agents that dynamically adjust wafer production parameters (temperature, pressure) in real time, reducing defects by 22%.   Shift: From rule-based automation → self-learning systems.   Step 2: Build a Federated Data Mesh Action: - Dismantle centralized data lakes: Deploy domain-specific data products (e.g., machine health, energy consumption) owned by cross-functional teams. - Example: An aerospace manufacturer created a “Quality Data Product” combining IoT sensor data (CNC machines) and supplier QC reports, cutting rework by 35%.   Shift: From centralized data ownership → decentralized, domain-driven data ecosystems.   Step 3: Adopt Composable Architecture Action: - Modularize legacy MES/ERP: Break monolithic systems into microservices (e.g., “inventory optimization” as a standalone service). - Example: A tire manufacturer decoupled its scheduling system into API-driven modules, enabling real-time rescheduling during rubber supply shortages.   Shift: From rigid, monolithic systems → plug-and-play “Lego blocks”.   Step 4: Enable Edge-to-Cloud Continuum Action: - Process latency-critical tasks (e.g., robotic vision) at the edge to optimize response times and reduce data gravity. - Example: A heavy machinery company used edge AI to inspect welds in 50ms (vs. 2s with cloud), avoiding $8M/year in recall costs.   Shift: From cloud-centric → edge intelligence with hybrid governance.   Step 5: Create a “Living” Digital Twin Ecosystem Action: - Integrate physics-based models with live IoT/ERP data to simulate, predict, and prescribe actions. - Example: A chemical plant’s digital twin autonomously adjusted reactor conditions using weather + demand forecasts, boosting yield by 18%.   Shift: From descriptive dashboards → prescriptive, closed-loop twins.   Step 6: Implement Autonomous Governance Action: - Embed compliance into architecture using blockchain and smart contracts for trustless, audit-ready execution. - Example: A EV battery supplier enforced ethical mining by embedding IoT/blockchain traceability into its EA, resolving 95% of audit queries instantly.   Shift: From manual audits → machine-executable policies.   Continue in 1st and 2nd comments.   Transform Partner – Your Strategic Champion for Digital Transformation   Image Source: Gartner

What connects Industrial IoT, Application and Data Integration, and Process Intelligence? During my time at Software AG, my attention has shifted in line with the company's strategic priorities and the changing needs of the market. My focus on Industrial IoT, moved into Application and Data Integration, and now I specialise on Business Process Management and Process Intelligence through ARIS. While these areas may appear to address different challenges, a common thread runs through them. Take a typical production process as an example. From raw material intake to finished goods delivery, there are countless interdependencies, processes and workflows, and just as many data sources. Industrial IoT plays a key role by capturing real-time data from machines and sensors on the shop floor. This data provides visibility into equipment performance, production rates, energy usage, and more. It enables predictive maintenance, reduces downtime, and supports continuous improvement through real-time monitoring and analytics. Application and Data Integration brings together data from across the value chain, including sensor data, manufacturing execution systems, ERP platforms, quality management systems, logistics, and supply chain management. Synchronising these systems with integration creates a unified, reliable view of production operations. This cohesion is essential for automation, traceability, quality management and responsive decision-making across departments and geographies. Process Management, including modelling, and governance, risk, and controls, takes a different yet equally critical perspective. Modelling helps design optimal process flows, while governance frameworks ensure controls are in place to manage quality, risk, and enforce conformance for standardisation. Process mining uncovers bottlenecks, rework loops, and compliance deviations. It focuses on how the production process actually runs, rather than how it was designed to operate. Despite their different vantage points, each of these domains works toward the same goal: aggregating, normalising, and structuring data to transform it into information that can be easily consumed to create meaningful, actionable insights. If your organisation is capturing process-related data through isolated tools, such as diagramming or collaboration platforms, quality management systems, risk registers, or role-based work instructions, it is likely you are only seeing part of the picture. Without a unified approach to integrating and analysing this data, the deeper insights remain fragmented or out of reach. By aligning physical operations, applications & systems, and business processes, organisations can move beyond surface-level visibility to uncover the root causes of inefficiency, unlock hidden potential, and govern change with clarity and confidence. #Process #Intelligence #OperationalExcellence #QualityManagement #Risk #Compliance

Many factories lose money on problems they can't even see. Tiny defects, machine breakdowns, and small inefficiencies add up quietly. Regular robots and machines can't spot these issues. But AI can see them. The groundbreaking partnership between Intel and LG Innotek tackles this challenge head-on. We are building a smart factory where AI acts as a "superhuman eye" for real-time visual quality control. This system is powered by a suite of Intel technologies, including Intel® Xeon® processors, the OpenVINO toolkit, and Intel® Arc™ Graphics. This is a leap beyond simple robotics. We're now moving into the era of the self-optimizing production line. What does this look like in practice?  - AI vision systems can detect defects invisible to the human eye. Micro-fractures, subtle color variations, minute misalignments prevent flawed products from reaching the next stage. - As the AI analyzes thousands of units, it learns. It begins to identify patterns that predict a future failure, allowing for preemptive adjustments to the manufacturing process itself. - This creates a continuous feedback cycle. The line doesn't just produce widgets; it produces data. That data fuels the AI, which in turn makes the line smarter, more efficient, and more resilient with every shift. I see this as the fundamental shift from automated manufacturing to cognitive manufacturing. The goal is no longer just speed but intelligent adaptation.  Read more here: https://lnkd.in/gz6tURZz #IntelAI #SmartFactories #IntelXeon #IntelArc #AIInManufacturing

I believe AI creates real value when it tackles hard, physical problems — the kind that live in factories, warehouses, and service tasks. Recently, I learned the attached from a plastics machine manufacturer and logistics provider struggling with unpredictable production schedules, warehouse congestion, and reactive maintenance routines. When a structured AI implementation approach was brought into the equation the following outcome was achieved 👇 🔹 Smart Production Planning – Machine learning models forecasted demand and optimized resin batch production, cutting material waste by 18%. 🔹 AI-Driven Warehouse Logistics – Intelligent slotting and routing algorithms boosted order fulfillment rates by 25%, reducing forklift travel time and idle inventory. 🔹 Predictive Maintenance for Service Teams – Sensor data and pattern recognition flagged early signs of machine wear, reducing unplanned downtime by 30%. The result wasn’t automation replacing people — it was augmentation empowering people. Operators, warehouse managers, and service engineers gained real-time insights to make faster, better decisions. 💡 Takeaway: AI success in industrial environments isn’t about technology first — it’s about aligning data, people, and process to create measurable operational impact. #AI #IndustrialServices #SmartManufacturing #WarehouseOptimization #PredictiveMaintenance #DigitalTransformation #OperationalExcellence

Here’s what most Manufacturing AI leaders get wrong: They start with the tech. “What model should we use?” “Can we try GenAI for this?” That’s the fastest way to burn your AI budget. Here’s what actually works: Start by asking this: 👉 Where are we losing time or money on manual decisions and do we have data on those steps? Let’s break that down: 🔍 Step 1: Spot the friction - Look for: Repetitive tasks (scheduling, inspection, calibration) Frequent decisions made by humans under pressure Any workflow where small mistakes cost big money 📊 Step 2: Check for data - Ask: Do we collect timestamps, sensor logs, machine status, operator input? Can we trace what decisions were made, by whom, and when? 💥 Step 3: Now, apply AI - Examples that actually move the needle: Predictive maintenance from vibration data AI-driven scheduling based on real-time bottlenecks Defect detection using existing camera feeds Most “AI projects” fail because they’re solving invisible problems with expensive tools. Here’s the truth: AI isn’t a magic wand. It’s a force multiplier. If your process is broken, it just breaks "faster." So forget buzzwords. Build better questions. That’s the real blueprint for impact. #manufacturing #AI #industrialAI #smartfactory #automation #aiops #productivity #digifabai #AIstrategy

AI-enhanced power grid optimization can reduce emissions that are the equivalent of removing 6.5 million (U.S.) gas-powered mid-size passenger vehicles from the road for a year. “AI” is a much broader term than what most people think of—it’s not all LLMs! When it comes to reducing energy waste and operational power grid emissions, AI can help by dispatching generation assets more optimally, reducing losses, congestion, and cost. In our paper, which will be presented at the NeurIPS 2025 Workshop "Tackling Climate Change with Machine Learning," we analyze the operational emissions associated with training CANOS, Google DeepMind’s graph neural network for solving AC Optimal Power Flow (OPF) on a 10,000-bus power system. We then estimate how emissions and energy use would change if these dispatch solutions were used to determine generator (power plant) dispatch decisions, instead of the status-quo linear approximations used in many power markets to set generator output. Especially compared to training something as complex as an LLM, training these GNNs—which have a focused task (learning OPF solutions)—“pays back” all energy and emissions costs associated with the model's training within a single hour. At a country-wide scale, operating the grid more efficiently using these models is approximately equivalent to removing 6.5 million (U.S.) gas-powered mid-size passenger vehicles from the road for a year. Of course, a full analysis would require a lifecycle carbon assessment of training these GNNs. And we'd have to run the actual power grid models themselves across ISOs, not just a 10,000 bus synthetic grid. Additionally, we'd need to model other grid components and concepts like ancillary services, self-schedulers, and more. But even if we’re off by, say, a HUNDRED times, the conclusion is still clear: using a GNN approximation for dispatch can reduce energy use and emissions relative to DC OPF-based approximations. (Even if we're off by the training emissions by a *thousand* times, this holds true.) If you’re at NeurIPS in San Diego this year, please come chat with me at the session if you’re interested in this work! Read more here: https://lnkd.in/g9aqhXpy And stop saying "AI" when you actually mean LLMs. :)

🇩🇪 Germany Builds AI-Controlled Smart Grid That Predicts Power Demand Hours in Advance German energy researchers have launched an artificial intelligence–driven smart grid platform capable of predicting electricity demand several hours ahead using real-time consumption data, weather forecasts, and industrial activity signals. The system automatically adjusts power distribution to reduce energy waste and prevent overloads. Pilot deployments demonstrated measurable reductions in blackout risk and improved renewable energy integration, as the AI system can rapidly shift supply between solar, wind, and storage systems based on predicted demand patterns. Specialists say predictive energy grids could dramatically increase infrastructure efficiency while supporting the transition to fully renewable national power systems.

I was honored to join Axios energy reporter Ben Geman at the Atlantic Council in Washington, DC, for a fireside chat to discuss what it will take to power an economy that’s more electrified, resilient and competitive. The reality is stark: demand for electricity is projected to grow far faster than overall energy use. This is no threat to prosperity; it’s an opportunity - if we act with realism and speed. I have three takeaways from our discussion, and they are based on one simple insight: a successful energy transition needs energy security. We need to put the technologies and infrastructure in place to ensure we have the right energy, at the right time, at the right price. We can achieve this if we: 1. Squeeze more from every kilowatt: Energy efficiency and grid modernization are just as important as energy supply. We can quickly improve energy efficiency in industries and buildings by using high-efficiency motors with variable-speed drives. If widely adopted, this could reduce electricity demand by about 10% - the same as the output from around 100 coal plants or 35 nuclear plants. These savings could meet the growing energy needs of data centers for several years. 2. Modernize and digitalize the grid: We are still trying to run a 21st century economy on 20th century infrastructure. By 2040, the world needs 80 million kilometers (almost 50 million miles) of grid upgrades, plus storage and digital control, to integrate variable renewables, balance peaks, and improve resilience. Permitting is now a critical bottleneck. This is where targeted policy – with smarter approvals, clear standards, and investment in distribution networks – can unlock real capacity quickly. 3. Make AI part of the solution: There are a lot of headlines that Artificial Intelligence is driving up demand for energy. However, AI-enabled energy management – with digital substations and edge control – can also optimize usage, reduce losses and prevent outages. We have to see AI as a crucial tool to manage grids, to forecast, shift and reduce demand. AI can help us align demand growth with grid reliability. None of this scales without people. Resilient energy systems need a skilled workforce, from electricians to data scientists. Upskilling, retraining, and apprenticeships have to be made a priority by both the public and the private sector. The path forward is clear: electrify everything you can; deploy efficiency first; digitalize the grid; and use AI to manage what we add (and have). For regions and countries that do this, energy security will be a competitive advantage creating the foundations for sustainable growth. Listen to the full discussion here: https://lnkd.in/emMu-4zr