Airbus Helicopters uses AI to optimize its helicopter production assembly lines for both safety and performance. Their teams extended an existing Red Hat OpenShift cluster so it could also run AI workloads, instead of standing up a separate, isolated AI environment.

With Red Hat OpenShift AI, they can:

• Run AI models directly where production systems already live, reducing integration friction.
• Analyze production data in near real time to detect issues on the line.
• Use insights from AI to adjust processes and improve both safety and operational efficiency.

This approach helps them reimagine how quality and performance are monitored, using a common platform for both traditional applications and AI-driven analytics.

AI-driven anomaly detection has a direct financial and operational impact for Airbus Helicopters. By using AI models on Red Hat OpenShift AI to spot irregularities in production data, they can catch issues that might otherwise go unnoticed.

One highlighted outcome is a cost avoidance of about 1 million euro thanks to improved anomaly detection. This comes from:

• Identifying flaws earlier in the assembly process.
• Reducing rework and scrap.
• Lowering the risk of a costly “one-in-a-million” flaw making it through to later stages.

In practice, this means AI is helping them rethink quality control, turning production data into a continuous feedback loop that protects both the bottom line and product reliability.

For Airbus Helicopters, keeping AI workloads on-premise is described as a “no-brainer” because of the sensitive nature of their data. Production and engineering data often involve strict security, compliance, and confidentiality requirements.

Red Hat OpenShift AI supports this by:

• Letting them run AI on the same on-premise OpenShift cluster they already use for other workloads.
• Providing a consistent, enterprise-ready platform for building, deploying, and managing AI solutions behind their own firewall.
• Reducing data movement to external environments, which helps with governance and risk management.

This setup allows them to reimagine AI adoption in a way that aligns with their security posture, while still scaling AI innovation across production operations.