United KingdomManufacturing2024 – ongoing

Rolls-Royce

Azure Databricks analytics and AI blade inspection

Rolls-Royce reports 30% higher machine use and about 400 prevented maintenance events a year

WorkingVendor's case studyVendor ↗

Problem

Engine design, production and fleet monitoring involved large data volumes and manual work. About 2 million cooling holes a month were inspected by hand before automation.

Approach

Rolls-Royce used Azure Databricks, Unity Catalog and GPUs for engine design. Machine learning and generative AI power a turbine blade inspection tool called Signature Analyzer, and cloud analytics monitor engine health.

Results

30%increase in machine utilization from AI-optimized blade inspectionVendor's case study[1]

about 400unplanned maintenance events detected and prevented per year across fleetsVendor's case study[1]

Takeaway

AI blade inspection raised machine utilization, and engine monitoring helped prevent unplanned maintenance. Microsoft put avoided costs at millions of euros without a breakdown.

Sources

Rolls-Royce customer story
Microsoft1 Apr 2025Vendor publication

Source summaries

[1]Microsoft1 Apr 2025

Microsoft describes Rolls-Royce's use of Azure Databricks and AI across engine design, blade inspection and health monitoring. It reports utilization and maintenance figures.

Full details are in the original sources.

Added 25 Oct 2026 · Updated 25 Oct 2026