Shriram Finance
less than 3–4 hours — Onboarding Turn-Around-Time (TAT)
Independently reportedBanking & finance · Process automation · Classical ML · In production
Process automation
GE Aerospace
Independently reportedManufacturingScaledEnterpriseClassical ML
Independently reported. An independent or peer-reviewed source reports this result, alongside the vendor.
Reported by enterpriseai.economictimes.indiatimes.com (independent reporting). Checked against the source page on 2026-09-06. 5 of 5 figures below appear on that page word for word.
The problem
Predictive maintenance for servicing commercial engine fleet with long time horizons.
What was deployed
- Products
- AI Wingmate, Blade Inspection Tool (BIT), Fuel Insight
- Technique
- Classical ML
- Build or buy
- Built in-house or to order
- Deployment
- Custom build
- Data used
- engine monitoring data
- Scale
- commercial engine fleet
What changed
Each row is quoted from the source. Figures we could not find on the page in those words are marked — they are kept, not deleted, so you can judge them.
lead times in detecting issues
“60 percent earlier lead times in detecting issues”
Quoted word for word from the source · round percentage, no baseline given
detection rates
“45 percent higher detection rates”
Quoted word for word from the source · round percentage, no baseline given
false alerts
“half the number of false alerts”
Quoted word for word from the source
one and a half hoursBlade inspection time (Blade Inspection Tool, GEnx engine)from three hours
“GE Aerospace cut inspection times in half, from three hours to one and a half, while improving accuracy.”
Quoted word for word from the source
15 to 20 percentDeveloper productivity gains (AI Wingmate / GenAI workflows)
“including 15 to 20 percent productivity gains for developers”
Quoted word for word from the source
hundreds of thousandsEmployee AI queries (AI Wingmate)
““hundreds of thousands” of employee queries that improve day-to-day execution”
Quoted word for word from the source
“We have been applying AI/ML models to build our predictive maintenance capabilities for servicing our commercial engine fleet for well over a decade.”
Sources
- enterpriseai.economictimes.indiatimes.com — independent reporting, 2026-01-07
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