How AI can Improve Digital Twin Technology

What if your AI could predict years of real-world performance after just days of testing? IBM Research has developed a new generation of AI-powered digital twins by applying foundation model techniques, the same deep learning architectures behind today's large language models (LLMs) to physical systems like batteries. Traditional digital twins (virtual simulations of real-world systems) have struggled because it’s incredibly hard to model the full complexity of physical systems accurately. IBM's innovation changes this: instead of manually building physics models, they train AI models on real-world sensor data to predict system behavior. These digital twins are data-driven, self-improving and can simulate complex behaviors with high precision. The first major application is in electric vehicle (EV) batteries, where IBM partnered with German company Sphere Energy. Developing and validating a new EV battery can take years because manufacturers have to physically test how batteries perform and degrade over time. Using IBM’s AI-powered digital twins, manufacturers can now simulate years of battery aging and usage after only a small amount of real-world testing. Sphere's models predict battery degradation within 1% accuracy, which wasn’t possible before with traditional simulations. Technically, IBM’s digital twins use a transformer-based encoder-decoder architecture (like a language model) but are trained on numerical sensor data (voltage, current, capacity, etc.) instead of text. Once trained, the model can generalize across different batteries or vehicles, needing only minimal fine-tuning — which saves huge amounts of time and money. The impact is huge: up to 50% faster development cycles, millions of dollars saved, and faster adoption of new battery technologies. Beyond EVs, this technology could also transform industries like energy, aerospace, manufacturing, and logistics by providing faster, real-time, AI-driven system modeling and predictive maintenance. Learn more: https://buff.ly/JAzctHa #IBM #IBMiX #AI#genAI

I have been engaged as an interim CTO by a large PE-backed firm investing billions in AI Datacenters, Powerplants, Microgrids, and Digital Infrastructure. One of the efforts I am leading is the build and deployment of a real-time digital twin to guide investments and operational decisions—sharing some thoughts below. Digital Twins Need to Grow Up—Fast... The narrative around digital twins is glowing: virtual models that mirror real-world infrastructure, unlocking better planning, risk reduction, and ROI. And yes—there’s truth there. But here’s what's missing: *Today’s digital twins are too static...to gimmicky* They’re often treated as a siloed asset—built for snapshots, not continuous living systems. They get updated on project timelines rather than in the heartbeat of live operations. By the time decisions are made from them, reality has evolved. The real power of digital twins will only be unlocked when they: - Operate in Real Time – They must be connected to live data streams across operations, not occasional imports. That means ingesting telemetry, sensor data, operational KPIs, environmental variables, and market signals the moment they happen. - Integrate into Decision-Making, Not Sit Beside It – A digital twin should be the operating brain, not a sidekick. Decision-makers need insights flowing directly from the twin into operational dashboards, control systems, and strategy tools. - Evolve into AI-Augmented Decision Agents – Imagine AI agents continuously scanning the twin’s data, comparing it against external factor— weather forecasts, commodity prices, geopolitical risk, equipment maintenance records—and making proactive recommendations in the moment. *What AI-Enhanced Digital Twins Could Look Like... - Energy & Renewables: AI agents could adjust wind farm blade pitch in real time based on incoming wind shear patterns, or automatically optimize battery storage discharge according to live market pricing. - Oil & Gas: Integrated pipeline twins could detect micro-changes in pressure, cross-check with environmental data, and reroute flows to prevent downtime. - Industrial Manufacturing: Digital twins could predict machinery wear weeks ahead by correlating production logs, vibration analysis, and supply chain forecasts—scheduling repairs before a breakdown disrupts output. - Utilities: Power grid twins could anticipate surges from EV charging patterns, weather events, or grid failures elsewhere, dynamically rebalancing loads in milliseconds. We need to move from representations to intelligent companions. From models that inform to agents that decide. And from after-the-fact updates to real-time operational orchestration. The future of infrastructure (digital and physical) — and the ROI everyone’s chasing—will depend on how fast we close that gap. #digitaltwins #realtime #renewables #datasilos #AI #industrial #manufacturing #energyindustry #AIaaP #DecisionAgents #AIAgents #ICSAgents "From Mirror Image to Living Intelligence"

One of the most transformative digital tools applied in #cement grinding is the #digitaltwin — a real-time virtual replica of physical equipment and processes. By integrating #sensordata and process models, digital twins enable engineers to simulate process variations and run “what-if” scenarios without disrupting actual production. These simulations support decisions on variables such as #grindingmedia charge, mill speed, and classifier settings, allowing optimisation of energy use and product fineness. Digital twins have been used to optimize #kilns and grinding circuits in plants worldwide, reducing unplanned downtime and allowing predictive maintenance to extend the life of expensive grinding assets. While #digital technologies improve control and prediction, materials science innovations in grinding media and grinding aids have become equally crucial for achieving performance gains. Traditionally composed of high-chrome cast iron or forged steel, grinding media account for nearly a quarter of global grinding media consumption by application, with efficiency improvements translating directly to lower energy intensity. Recent advancements include #ceramic and #hybridmedia that combine hardness and toughness to reduce wear and energy losses. For example, manufacturers such as Sanxin New Materials in China and Tosoh Corporation in Japan have developed sub-nano and zirconia media with exceptional wear resistance. Complementing #grindingmedia are grinding aids — chemical additives that improve mill throughput and reduce energy consumption by altering the surface properties of particles, trapping air, and preventing re-agglomeration. Technology leaders like SIKA AG and GCP Applied Technologies have invested in tailored grinding aids compatible with AI-driven dosing platforms that automatically adjust additive concentrations based on real-time mill conditions. Trials in South America reported throughput improvements nearing 19% when integrating such digital assistive dosing with process control systems. The integration of grinding media data and digital dosing of grinding aids moves the mill closer to a self-optimizing system, where AI not only predicts media wear or energy losses but prescribes optimal interventions through automated dosing and operational adjustments. Heidelberg Materials has deployed digital twin technologies across global plants, achieving up to 15% increases in production efficiency and 20% reductions in energy consumption by leveraging real-time analytics and predictive algorithms. Holcim’s Siggenthal plant in Switzerland piloted AI controllers that autonomously adjusted kiln operations, boosting throughput while reducing specific energy consumption and emissions. Cemex, through its AI and #predictivemaintenance initiatives, improved kiln availability and reduced maintenance costs by predicting failures before they occurred. Read my full article in the February’26 issue of Indian Cement Review.

Digital twins began as mirrors of operations, useful but descriptive, reflecting what is rather than letting teams rehearse what should happen. Recent research pushes a step further with semantic twins that encode rules, constraints, and relationships directly from unstructured text into executable knowledge graphs. In one case study, LLMs extract regulatory and design constraints, formalise them as RDF, and drive simulations that stay compliant as conditions change. This shift is profound beyond infrastructure. When policy, process, and risk become machine-readable, you can preview choices and see consequences before spending or risking anything. Without a semantic layer, a twin is another dashboard, descriptive rather than decisive. Add semantics, and it becomes a rehearsal space for judgment, where agents on rails explore scenarios safely and every action leaves an auditable trail. This is how we move from app silos to workflows, from diagrams to living processes, and from demos to state change backed by evidence. I keep returning to a simple claim that feels increasingly obvious in practice: preview first, then build, because simulated failure is cheaper than real-world failure. A good twin lets AI discover better flows, turns processes into living, queryable objects, and makes innovation routine by eliminating downside risk. If agents are workflows that act, remember, and spend, then semantic twins are the rails that keep them aligned with policy, context, and outcomes. This research even shows regulation-aware optimisation and hurricane simulations expressed as RDF states, each operational change traceable and testable later. Over the next few months I’ll be writing more about digital twins, semantics, and receipts, because the architecture is finally catching up with the promise. I know that because I’m watching it being built by the chap at the front of that promise.

In my original post, I outlined five shifts shaping the evolution of GIS in the AI era. I then explored Geospatial Foundation Models as the technological engine, and Conversational GIS and Spatial RAG as the interface layer democratizing access to spatial intelligence. Today, I want to focus on the third shift: Predictive Digital Twins. ◉ From visualization to simulation Digital twins are not new. Many cities, utilities, airports, and campuses already maintain 3D models of their assets and environments. What is changing is their purpose. With AI integrated into GIS platforms, digital twins are evolving from static representations into predictive simulation environments. They no longer just show what exists. They help anticipate what could happen next. ◉ What makes a digital twin predictive? A predictive digital twin fuses multiple layers: Authoritative GIS data Building and infrastructure models Real time IoT and sensor feeds Climate projections and risk layers AI driven simulation and pattern detection This combination allows leaders to run forward looking scenarios, not just visualize current conditions. An urban planner can simulate the impact of a new transit corridor on congestion patterns and land use over time. A coastal city can model how different sea level rise scenarios will affect specific neighborhoods and infrastructure assets. An energy provider can test how grid performance responds to extreme heat combined with peak demand. ◉ Why this matters strategically Capital allocation decisions are long term and expensive. Infrastructure, transport, utilities, and climate resilience projects often shape communities for decades. Predictive digital twins allow organizations to test assumptions before committing resources in the physical world. They reduce uncertainty and improve risk management by making complex system interactions visible and measurable. ◉ The role of GIS At the core of every meaningful digital twin is a robust geospatial foundation. Location provides the organizing framework that connects assets, demographics, environmental variables, and risk models. Without a strong GIS architecture, a digital twin becomes a 3D visualization tool. With it, it becomes a decision platform. From where I sit, predictive digital twins represent the convergence of GIS, AI, and operational systems into a single strategic capability. They move spatial technology from descriptive insight to anticipatory intelligence. In the next post, I will explore the fourth shift: Edge Intelligence and Autonomous Updates.

From Replica to Intelligence: How AI Transforms Digital Twins Most people think of a digital twin as a static replica. But with AI, it becomes something far more powerful: a system that thinks, predicts, and adapts. Here’s what changes when AI steps in: 🔧 Predictive Maintenance No more waiting for breakdowns. AI spots failure patterns before they happen—cutting downtime and costs. ⚙️ Process Optimization AI runs endless “what if” scenarios inside the twin to fine-tune performance, automatically. ⚡ Real-Time Decision Making Sensor data → instant insights. AI turns raw feeds into recommendations you can act on immediately. 🛡 Risk Management Simulations test risk scenarios in advance, giving you playbooks before problems hit. The shift? 👉 A digital twin without AI is a snapshot. 👉 A digital twin with AI is a living, learning advisor for your business. 💡 The companies leading in 2025 aren’t just monitoring assets. They’re using AI-powered twins to anticipate the future and outpace competitors. #DigitalTwin #AI #PredictiveAnalytics #FutureOfWork

Industrial Digital Twins: Vendor Update Q1 2026 🏭 🔹 Siemens Launched Digital Twin Composer + industrial copilots → bringing real-time data + simulation + AI into one environment 🔹 NVIDIA At GTC: unified ecosystem (Siemens, PTC, Dassault, Cadence) on Omniverse + CUDA-X → enabling agentic AI workflows across design → simulation → factory 🔹 PTC Connected Onshape to NVIDIA Isaac Sim → direct path from CAD → robotics simulation → deployment 🔹 Dassault Systèmes Expanded NVIDIA partnership for AI-powered virtual twins on 3DEXPERIENCE → pushing deeper into AI-driven engineering workflows 🔹 AVEVA Introduced lifecycle digital twins for AI factories → targeting gigawatt-scale data centers + GPU optimization 🔹 Schneider Electric + ETAP Software Launched physics-based grid digital twin → real-time simulation for power systems + infrastructure resilience 🔹 Bentley Systems Scaling digital twins in utilities + grid infrastructure → highlighting data integration as the key bottleneck 📌 Broader Shifts in Q1 2026 • CAD → Physics → Operational Workflows Market momentum is focused on reducing the friction between CAD/PLM, simulation (physics engines), and real operational data, making digital twins executable and continuously updated. • Simulation → Real‑Time Control Layers Vendors are turning twins into real‑time operational and planning systems (end-to-end), not just offline analysis models. • Digital Twin → AI Decision Layer Across the ecosystem, AI (agentic or predictive) is being embedded into twin platforms to assist with optimization, anomaly detection, and autonomous design/engineering workflows. • Open Platforms & Standards OpenUSD and shared ecosystem APIs (e.g., Omniverse Cloud APIs) are becoming a foundation for industrial twin interoperability across tools.

The Future of Enterprise Architecture Is a Digital Twin - Here’s How to Get There In an era of constant disruption, traditional Enterprise Architecture (EA) is evolving fast. Static blueprints, annual refresh cycles, and siloed diagrams no longer cut it. The future of EA isn’t another framework update, it’s a living, real-time virtual replica of your entire organization. Sound like a Digital Twin? Exactly. In the same way you implement Digital Twins in the manufacturing environment to mirror physical assets (factories, supply chains, even jet engines) with live sensor data for simulation and optimization, forward-looking enterprises are building an Enterprise Digital Twin (EDT), or Digital Twin of the Organization (DTO). This isn’t science fiction. It’s the next logical step for maturing your EA: a dynamic model that continuously syncs business capabilities, processes, applications, data flows, and technology with operational reality. Why does this matter? An EDT lets you run “what-if” scenarios in minutes instead of months, test cloud migrations, AI rollouts, or regulatory changes without risking real operations. It turns EA from a rear-view mirror into a predictive cockpit for strategy, risk, and value delivery. DTOs are expected to become standard, with EA serving as the structural backbone that connects static models to live data streams. The payoff? Faster decisions, lower transformation risks, proactive optimization, and true organizational agility. To move from today’s static EA to a fully functional EDT, here is a summary of the top foundational capabilities you need to focus on. Prioritize them in sequence, they build on each other. 1) Real-Time Data Integration and Synchronization: Unified data layer that ingests your architecture asset data. 2) Dynamic Multi-Layer Modeling Platform: Extend your core EA repository with a platform that supports architecture description standards but adds real-time updates and simulation. 3) AI-Powered Analytics and Simulation Engine: Layer in AI and ML for insights, anomaly detection, auto-model updates and MAGIC HAPPENS! 😉Agentic AI can then make governance decisions. 4) Robust Simulation and Scenario-Planning Tools: Establish “digital sandboxes” for testing, technology rationalization, or process redesigns with visualizations. 5) Enterprise Governance, Security, and Collaboration Framework: Time to get the humans engaged with role-based access via data and simulation literate teams. Implementing these doesn’t require a rip-and-replace. Start small: pilot a focused twin for one value stream or capability, prove ROI, then scale. Many organizations are already leveraging modern EA platforms enhanced with real-time connectors and AI to accelerate the journey. My favorites: #Ardoq, #Peaqview, #LeanIX The organizations that treat EA as a dynamic Digital Twin today will outpace competitors tomorrow, making smarter bets, failing fast in simulation rather than reality.

🌟 𝐀𝐈 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐓𝐰𝐢𝐧𝐬 𝐢𝐧 𝐀𝐜𝐭𝐢𝐨𝐧: 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐒𝐭𝐨𝐫𝐢𝐞𝐬 🏆 Curious about how AI-driven digital twins are transforming real supply chains? Let's dive into some eye-opening examples that are setting new industry standards: 𝟏. 𝐔𝐧𝐢𝐥𝐞𝐯𝐞𝐫'𝐬 𝐂𝐫𝐲𝐬𝐭𝐚𝐥 𝐁𝐚𝐥𝐥 ✨ Unilever created a digital twin of its global supply network, enabling it to run millions of scenarios in minutes. Result? They've significantly improved their ability to respond to sudden market changes and disruptions. 𝟐. 𝐃𝐇𝐋'𝐬 𝐒𝐦𝐚𝐫𝐭 𝐖𝐚𝐫𝐞𝐡𝐨𝐮𝐬𝐞𝐬 📦 DHL uses AI-powered digital twins to optimize warehouse operations. By simulating different layouts and workflows, they've boosted efficiency by up to 20% in some facilities. 𝟑. 𝐏𝐨𝐫𝐭 𝐨𝐟 𝐑𝐨𝐭𝐭𝐞𝐫𝐝𝐚𝐦'𝐬 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐌𝐚𝐤𝐞𝐨𝐯𝐞𝐫 🚢 The busiest port in Europe uses AI digital twins to optimize ship movements, reduce wait times, and lower emissions. They're reshaping the future of maritime logistics. 𝟒. 𝐒𝐢𝐞𝐦𝐞𝐧𝐬' 𝐅𝐚𝐜𝐭𝐨𝐫𝐲 𝐨𝐟 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 🏭 Siemens' Amberg Electronics Plant uses a digital twin to simulate production processes, resulting in a 99.9% quality rate and 75% reduction in defects. 𝟓. 𝐀𝐦𝐚𝐳𝐨𝐧'𝐬 𝐈𝐧𝐯𝐞𝐧𝐭𝐨𝐫𝐲 𝐂𝐫𝐲𝐬𝐭𝐚𝐥 𝐁𝐚𝐥𝐥 🔮 Amazon leverages AI and digital twins to predict inventory needs and optimize fulfillment center operations, contributing to their famous same-day delivery capabilities. 𝟔. 𝐁𝐌𝐖'𝐬 𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐕𝐞𝐡𝐢𝐜𝐥𝐞 𝐁𝐢𝐫𝐭𝐡 🚗 BMW uses AI digital twins in their production process, simulating every step from design to manufacturing. This has cut production planning time by 30%. 𝟕. 𝐌𝐚𝐞𝐫𝐬𝐤'𝐬 𝐖𝐞𝐚𝐭𝐡𝐞𝐫-𝐁𝐞𝐚𝐭𝐢𝐧𝐠 𝐑𝐨𝐮𝐭𝐞𝐬 ⛵ Maersk employs AI digital twins to optimize shipping routes based on real-time weather data, reducing fuel consumption and improving delivery times. 𝐓𝐡𝐞 𝐜𝐨𝐦𝐦𝐨𝐧 𝐭𝐡𝐫𝐞𝐚𝐝? These companies see tangible benefits: reduced costs, improved efficiency, enhanced resilience, and happier customers. 👥 𝐘𝐨𝐮𝐫 𝐭𝐮𝐫𝐧: Have you encountered AI digital twins in your supply chain journey? Share your experience or thoughts below! #SupplyChainInnovation #AIinAction #DigitalTwins #RealWorldAI #SupplyChainTech #IndustryLeaders #DigitalTransformation #SupplyChainSuccess #Tech2024 #FutureOfLogistics

AI isn’t just transforming digital work — it’s transforming physical industries. Unilever just rolled out AI-driven digital twins to simulate manufacturing, optimize R&D, and reduce waste across its factories. The results? • 8% higher equipment effectiveness • 20% less waste • Faster product development cycles • Predictive insights that prevent downtime This marks a major shift: From testing in the real world → to testing in AI-driven simulations that run hundreds of scenarios before a single machine starts up. For global manufacturers, this isn’t a nice-to-have — it’s a competitive advantage. 💬 Do you believe AI-driven digital twins will become the default operating model for manufacturing?