Building AI prototypes to front load user testing
Utilizing agentic AI in personal workflows to facilitate roadmap throughput.
Situation
As with any company, developer bandwidth comes at a premium. Every inefficiency in process cascades into slower time to market, delayed roadmap delivery, and missed ROI. At Protobrand, staying ahead of competitors is crucial, and thus we treat time to market as our most important metric.
Task
Bring a solution to market in the fastest, most effective way. Decrease the expected time to market.
The product was moving the analysis plan from an asynchronous document to a fully integrated on-platform page, embedded into the core workflows of our platform, Meta4 Insight.
KPIs:
- Time to market (expected vs. actual)
- User adoption of analysis plan (% of projects that use an analysis plan)
Action
We used agentic AI to build working prototypes for user testing, completing A/B tests, usability tests, and user acceptance tests before a single developer touched the project. A working prototype gives far more accurate measurements for task efficiency, time on task, and click count than a static Figma file ever could.
Front-loading user testing this way meant we avoided feeding developers a moving target of human feedback-induced changes.
Result
The prototype testing was a success. Early tests helped us decide the design direction, identify user pain points, and validate the approach, all without any developer intervention.
By front-loading user testing without touching developer bandwidth, the time to market from developer inception decreased by 37.5% for this project.
User adoption of the analysis plan feature is yet to be determined, as this project was recently released.