Lessons from real-world delivery
AI-assisted testing: What engineering leaders need to know
Your team is shipping faster with AI. But is it really shipping better?
Based on a controlled experiment and real client projects, this whitepaper explains how to scale testing with AI while maintaining high-quality output. You’ll learn:
Where AI actually delivers results in testing (and where it doesn’t)
Where AI creates false confidence
How spec quality directly impacts test accuracy
How to build validation into your pipeline without slowing delivery
Two teams. One experiment.
In 2025, we ran a controlled experiment. Two teams were tasked to work on the same product, but only one could use AI.
The AI-assisted team, despite being smaller, delivered the same scope 45% faster. However, the difference in testing is where the results got interesting. See the best practices that this real experiment, combined with real-world delivery, taught us.
Only 43% of teams use AI for QA & testing
Accelerating development with AI while leaving testing behind creates a gap. You might ship faster, but not necessarily better. Over time, that gap shows up in bugs, rework, and production issues.
Our original research shows that 84% of teams use AI in product development, but most don’t use it for testing. Learn the use cases for AI-assisted testing that deliver reliable outcomes.
An essential read for QA experts
When testing evolves alongside AI, you catch issues earlier, preserve signal integrity across your pipelines, and scale delivery without losing control. See how AI-assisted testing helps you move faster while maintaining the quality your users expect.