Normalized claim
Time: 30 seconds decrease
Panel visualization creation time reduced to 30 seconds, code only needs 20% manual adaptation.
Last evidence check: Jul 22, 2026
Siemens Industrial Copilot Powers Automation and Quality Improvements in Manufacturing
Use case typeManufacturing quality monitoringUpdated Jun 13, 2026
Siemens, in partnership with Microsoft, has launched the Siemens Industrial Copilot, a generative AI-powered agent based on Azure OpenAI Service. The Copilot is transforming manufacturing operations at over 100 companies across Europe and the US, including thyssenkrupp Automation Engineering. It automates complex engineering tasks such as sensor configuration and reporting, streamlines workflows, and combats skilled labor shortages. Leveraging deep integration into Siemens engineering software and Xcelerator platform, the Copilot reduces engineering cycle times and enables scale across product lines, with plans for global rollout by thyssenkrupp. The solution supports advanced manufacturing, including battery quality assurance for electric vehicles, and enhances operational resilience and sustainability. Over 120,000 engineers are able to use the Copilot, emphasizing its wide impact and usability in real-world scenarios. The integration with Azure provides security, reliability, and scalability for industrial automation.
- Organization
- thyssenkrupp Automation Engineering
- Industry
- Manufacturing
- Location
- Germany
- Published
- October 2024
Reported outcomes
−20%
timeTime & speed
30 secondstime
Strategic outcomes
New product / capabilityAutomated repetitive engineering tasksSpeed & agilityEnabled rapid engineering workflowsMarket & geographic expansionRolled out globally across automation businessCustomer experience & trustImproved manufacturing quality assurance
Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →
Why do we believe this?Outcome claims, sources, and evidence checks
Normalized claim
Time: 20% decrease
Panel visualization creation time reduced to 30 seconds, code only needs 20% manual adaptation.
Last evidence check: Jul 22, 2026
Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks
- Customer
- thyssenkrupp Automation Engineering, Siemens
- Provider
- Microsoft
- Maturity
- Production
- Linked source
- news.microsoft.com
The solution supports advanced manufacturing, including battery quality assurance for electric vehicles, and enhances operational resilience and sustainability
Customer identity supportedSource describes one deploymentMaturity supported
Primary read
Use case focus
Showing 3 of 3
- 1industrial copilot
- 2automation engineering assistant
- 3battery quality assurance
- Manufacturers face skilled labor shortages limiting productivity.
- Manual data management, sensor configuration, and reporting are time-intensive and error-prone.
- Maintaining strict quality requirements in sectors like battery production for electric vehicles is complex.
- Manufacturing environments demand scalable, resilient, and compliant AI solutions.
- Siemens Industrial Copilot automates repetitive engineering tasks and documentation.
- Integrates with Siemens engineering software in the cloud via the Xcelerator platform.
- Azure OpenAI Service powers GenAI capabilities for language understanding and task automation.
- Copilot supports real-time engineering with instant answers and rapid code generation.
Technologies
- thyssenkrupp to roll out Copilot globally, impacting its entire automation business.
- Panel visualization creation time reduced to 30 seconds, code only needs 20% manual adaptation.
- Addresses labor shortage and boosts manufacturing innovation across over 100 customer companies.
Architecture
Siemens Industrial Copilot integrates Azure OpenAI Service with Siemens Xcelerator platform for automation engineering. Engineers interact with the Copilot for task automation (sensor configuration, reporting, coding), leveraging Siemens software; the cloud architecture provides scalability, security, and real-time data exchange.
Sources & evidence4
Evidence: High85/100Evidence strength
- Customer explicitly identified
- Deployment status explicitly supported
- Independent source available
- Quantified outcome available
- Technical implementation details available
- Multiple corroborating sources available
- Recent evidence check available
- Last evidence check: Jul 22, 2026.
ExpandedExpanded
The same organization appears in newer AI deployment evidence.
- Same organization re-documented as recently as 2026.
- Cited source last checked Jun 12, 2026 — ok (0/4 broken).
Measures whether this deployment's public evidence persists — not whether the system is still in production.
Type: News ArticlePublished: Oct 24, 2024Publisher: news.microsoft.comEvidence: SecondaryConfidence: Low
AI-generated summary. Verify important details with the linked sources before relying on this case.
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