AI in Action Creating value for our customers
GE Aerospace has built one of the aviation industry’s largest AI patent portfolios through years of investment in supercomputing and digital technologies. Today, we apply AI to accelerate Safety, Quality, Delivery, and Cost — in that order — to support customers.
Design
Cutting design times.
Generative AI Design
Accelerates preliminary engine and propulsion design to reduce design times.
Supercomputing
Accelerates breakthrough engine designs like Open Fan.
AI for Materials
Collaborated with DARPA using AI to demonstrate cutting-edge materials for hypersonic vehicles.
Manufacturing
Improving engine delivery.
AI Engineering Assistant
Uses AI trained on 30 years of part data to inspect new parts, reducing thousands of hours in the evaluation process.
Agentic AI for Production Readiness
Multi-year partnership with Palantir Technologies to predict demand and identify constraints earlier.
Services
Reducing turnaround time.
AI Material Assistant
Predicts needed parts during engine shop visits months ahead, cutting turnaround times by 5-7 days.
AI-Enabled Blade Inspection Tool
Guides turbine blade image review to improve inspection consistency and reduce process time by half.
AI-Guided White Light Robot Inspection
Provides a second set of eyes for accurate and consistent inspections, helping technicians make the right call.
AI Impact
How we’re using AI at scale to support customers
24x7
AI-powered engine safety and health monitoring
60
%Faster preventative maintenance identification with AI
50
%Faster on-wing blade inspection time compared to past approach
2X
Investment in AI in 2026 compared to previous year
Agents of AIWe need skilled workers and AI to work together to meet strong demand in aviation and to support our customers.
Read more about the people behind the technology and those using it.
Guiding Principles for Responsible AI Use
As GE Aerospace evaluates, develops, and explores new applications of AI, it follows three guiding principles:
Trust
We use trusted and reliable data. We take a methodical, structured approach to selecting the right data to train our AI models.
Transparent
We require system transparency and repeatability. Our teams must have a clear understanding and confidence of an AI model’s insights and outputs.
Human
We keep a human in the loop. While our AI systems enable faster workflows, human experts ultimately make the final decisions.