Siemens uses this booth tour to show how its industrial AI strategy connects automation hardware, engineering software and domain-specific copilots into one digital enterprise stack. From the central Industrial AI Hub, Tsvetelina Nikolova explains how manufacturers can merge real-world production assets with a comprehensive digital twin, then run “what-if” scenarios across the entire lifecycle to optimize design, throughput and energy use. The focus is on leveraging Siemens Xcelerator, Industrial Operations X and Industrial Edge to turn heterogeneous shop-floor data into a consistent, AI-ready data fabric that spans OT and IT. https://www.siemens.com/global/en/products/automation/topic-areas/industrial-ai.html
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On the design and engineering side, the tour highlights generative AI embedded directly into NX and the new family of Industrial Copilots. Here, engineers can ask natural-language questions about CAD models, get design variants for components like TV wall mounts, or have NX CAM Copilot propose optimized toolpaths for complex parts. The Engineering Copilot TIA, tightly integrated with TIA Portal, lets automation engineers describe intents instead of writing or searching through PLC code, automating configuration tasks and documentation across projects. ([Siemens Press][3]) This reduces repetitive work, accelerates commissioning and makes it easier for new engineers to contribute quickly to established control architectures, improving productivity across the engineering workflow.
In operations, the video zooms in on Insights Hub, Siemens’ industrial IoT platform that aggregates sensor, PLC and MES data and exposes it through dashboards and a built-in copilot. Operators can use conversational queries to check stock levels in the MES, configure machines for short product runs with multiple variants, and orchestrate workflows textually rather than through custom scripts. The same data backbone feeds asset intelligence and predictive maintenance, illustrated by a BlueScope steel case where a digital twin “fingerprint” of critical assets is compared continuously with live data to detect deviations and trigger proactive interventions, avoiding roughly 2,000 hours of unplanned downtime. Together, these examples show how industrial AI copilots move from nice-to-have dashboards to closed-loop decision support that protects throughput and uptime.
The second half of the tour steps into Siemens’ “future” zone, where agentic AI and autonomous production concepts are on display. A robot cell is configured as an example of how autonomous agents could handle configuration, scheduling and execution of tasks, while an orchestrator agent coordinates specialized agents for planning, quality, logistics and energy optimization. Rather than replacing humans, Siemens positions these agentic systems as collaborators that take over low-level reconfiguration work so engineers and operators can focus on high-value problem solving, governance and safety. This aligns with Siemens’ broader push toward industrial foundation models and AI agents that can reason over engineering data, shop-floor events and business constraints across the wider industry.
Filmed in the Siemens hall at SPS 2025 in Nuremberg, the video also touches on how the company extends this experience beyond the physical stand through live talks and a persistent virtual booth. Nikolova stresses that AI-driven factories are still built around human decision-makers, with copilots and agents acting as transparent, explainable tools rather than opaque black boxes. For younger engineers, that means fewer hours on translation, documentation and repetitive configuration, and more time on creative tasks like new machine concepts or process improvements. The result is a glimpse of how industrial AI, digital twins and autonomous agents may reshape factory work over the coming years, while keeping human expertise firmly at the center of the experience online.
