The Challenge: Going from a strong engineering team to a AI-native one
Bubble is the only no-code AI app builder, powering AI-driven app development for over 6 million builders worldwide. AI-native development is core to Bubble’s mission, and the engineering team set out to live that ambition internally: accelerating AI adoption across the engineering organization and ensuring it was producing real impact, not just activity.
That ambition quickly ran into the limits of existing tooling: activity metrics scattered across disconnected dashboards failed to connect the dots on how AI was changing engineering outcomes. Managers were stuck interpreting raw numbers without a clear plan for what to do next. And when leaders ran experiments to advance AI adoption, there was no reliable way to know whether they worked. To realize their vision, Bubble’s leaders needed a unified understanding of AI’s true impact, prioritized actions to drive it further, and a way to measure every experiment.
“Before Quotient, we had only an incomplete, qualitative picture of productivity and morale. We were trying to drive an engineering transformation without a clear picture of what was working and what investments to make.”
The Solution: A trustworthy view of AI, and a clear plan to act on it
Quotient combined Bubble's engineering tooling data from source control, project management, and AI tools with unique developer experience data the team hadn't had access to before. Together, these gave Bubble a single, trustworthy correlation between AI usage and engineering effectiveness, and a foundation for delivering focused value at every layer of the organization.
Engineering managers can now identify the specific bottlenecks holding their teams back and leverage customized, research-backed actions to resolve them. The VP of engineering can now spot gaps across the AI-native operating model and apply best practices to close them. And executive leaders can now understand how AI usage is correlating with engineering effectiveness, with impact tracking that quantifies the ROI of their efforts in AI adoption.
Since adopting Quotient, Bubble has:
- Achieved a 51% improvement in AI adoption in 3 months across breadth, depth, and output metrics
- Driven a correlated 37% improvement in velocity, 32% reduction in cycle time, and 19% improvement in code quality.
- Built a repeatable experimentation motion that lets every team identify and resolve engineering bottlenecks
“Our teams now have a unified view of where AI is paying off, a clear plan to push it further, and tools to measure whether each experiment worked. That loop is what AI-native engineering actually looks like in practice.”
The Impact: Living what they build
Bubble’s engineering organization now operates the way their product asks customers to operate: AI-native, evidence-driven, and continuously iterating. Leaders see one trustworthy picture of AI’s impact. Managers act on prioritized, research-backed recommendations specific to their teams. And every AI experiment Bubble runs comes with a built-in way to measure whether it worked.
“Quotient is how we make sure our own engineering organization stays at the leading edge of AI as it evolves. We can now invest in our engineering culture and show the ROI of our AI investments in a way we were never able to do before.”
Why engineering leaders choose Quotient
Quotient equips Bubble with:
✓ A unified, trustworthy understanding of AI adoption and effectiveness, not just activity
✓ Customized, research-backed actions that turn insight into outcomes at every level of the org
✓ A built-in way to measure the ROI of every AI experiment, so improvement compounds
