95% Are Using GenAI for Test Data. Only 10% Trust It at Scale. Why?

We’re back again at GenRocket, and this time we’re tackling a hard truth many teams are quietly running into:

GenAI has made synthetic data easy to generate—but still hard to trust.

The numbers tell the story.
95% of organizations are already using GenAI to generate test data.
89% are piloting or actively using GenAI platforms.
Yet only 10% have fully integrated GenAI into their test data lifecycle—and just 15% have scaled it enterprise-wide.

Why the stall?

Because speed isn’t the problem—engineering is.

Quality Engineering doesn’t just need plausible data. It needs data that is repeatable, governed, secure, and purpose-built for testing. And that’s where cracks start to show. Teams report persistent challenges like lack of quality test data (51%), difficulty creating large datasets (49%), accuracy issues (48%), and compliance risk (47%).

In other words, the bottleneck isn’t automation.
It’s the data beneath it.

This is why GenRocket takes a fundamentally different approach. Instead of probabilistic outputs, we design synthetic data deterministically—from metadata, not production data. Every dataset is engineered to match test objectives exactly, including edge cases, negative paths, and scale. Same design in. Same data out. Every time.
That’s how experimentation turns into confidence.

Curious how design-driven synthetic data changes the game for enterprise QE?
Read the white paper and see what it really takes to move from GenAI pilots to production-ready scale.