How General Electric (GE) Leveraged Digital Twins and AI to Reduce Downtime and Improve Output

By: GoBeyond Team

July 3, 2025

3 min read

AI case study for GE – 40% less downtime and 20% cost reduction with digital twins

Quick Overview

GE deployed its Predix platform to create digital twins of equipment, simulate performance, and predict maintenance needs. Advanced analytics process sensor data for real-time insights. AI optimizes fuel consumption and operational parameters, reducing costs and improving output across sectors like energy and aviation.

General Electric (GE)

Company Size

~168,000 employees

Revenue Range

$80B+ annual revenue

Primary Challenge

Reducing unplanned downtime and maintenance costs while improving operational efficiency

Key Metrics

\- 40% reduction in unplanned downtime\- 20% cut in maintenance costs\- 10% improvement in output\- Over 1.2 million digital twins created generating $600B+ in value

The Problem

Unplanned equipment failures and inefficient maintenance increased costs and reduced productivity.

The Solution

Developed “GENIX” digital twin framework and Predix cloud platform to model assets digitally, enabling predictive maintenance and operational optimization using AI and machine learning.

Results

\- Significant reduction in downtime and maintenance costs\- Improved operational efficiency and output\- Enhanced decision-making with real-time asset insights\- Enabled predictive maintenance across multiple industries