# Leading Aviation Company

A flag-carrier airline transformed engineering decisions with AI attribution and delivery metrics.

## AI Engineering Intelligence

**Q1 2026**

### Results Overview

- **Prompt to merge:** -39%  
  Before: 6.2h  
  After: 3.8h

- **Rework rate:** -50%  
  Before: 22%  
  After: 11%

- **AI lift on velocity:** +100%  
  Before: 0%  
  After: 28%

**12-week trend** trending up

## The Challenge

With 200+ engineers spread across mission-critical systems, leadership needed hard numbers to justify continued investment in AI coding tools. Gut-feel arguments did not survive contact with the CFO, and there was no way to compare AI lift across teams or repositories.

## The Solution

iftrue connected every GitHub repo and Jira board to a unified AI-impact view. Prompt-to-merge time, rework rate, and velocity lift are now tracked per team and per AI assistant, turning quarterly roadmap defenses into data walkthroughs instead of debates.

## The Results

Tracked over two quarters of AI tool deployment.

### Prompt to merge

- **-39%**  
  Before: 6.2h  
  After: 3.8h  
  Median time from first AI prompt to merged PR.

### Rework rate

- **-50%**  
  Before: 22%  
  After: 11%  
  Half as many PRs require significant post-merge rework.

### AI lift on velocity

- **+100%**  
  Before: 0%  
  After: 28%  
  Measured sprint velocity gain attributable to AI assistants.