Iterate Earlier and Faster in B2B Discovery with AI-Assisted Prototyping

In B2B logistics software, workflows are data-heavy and mission-critical. Each customer has their own specific workflows and data variations. Traditional prototypes with placeholder data can't capture this reality—and that creates a blind spot. Users can't give meaningful feedback until they see how a solution handles their actual data, edge cases, and terminology. This delays the conversations that matter most.
We started experimenting with AI-assisted prototyping to change this. Using tools like Cursor, we built prototypes with much higher data and behavior fidelity during discovery—something that would typically require developer time we didn't have. The AI handled the complexity, letting us work with real customer data before any engineering resources were committed.
I'll share how we integrated AI into our discovery process and what we're learning—how increased fidelity shifted our focus to the problems that actually matter earlier in the process, how it changed the types of conversations we had with users and stakeholders, and how it enabled earlier collaboration between UX, product, and engineering. I'll also cover where this approach helped (and where it complicated things).