Jun 26, 2025 | 5 minute read
written by Bryan House
There’s a shift happening inside modern product teams. It’s subtle, fast, and undeniably real. It starts with a simple prompt: “As a user, I want to…”
What used to kick off a months-long cycle of specs, designs, and meetings now sparks something else entirely: a working prototype, built by AI in under an hour.
This trend — increasingly known as vibecoding — is changing how teams communicate, explore, and validate ideas. Rather than writing production-grade software with AI, it’s about prototyping with just enough fidelity to move a conversation forward.
The buzz around vibecoding is real. So is the skepticism. But beneath the noise, something important is happening: AI-generated prototypes are reshaping the dynamic between product and engineering. B2B commerce teams, with their deep logic and complex workflows, are some of the biggest beneficiaries.the-old-way
In most enterprise organizations, new product ideas have traditionally started with documents, and lots of them. Product managers write lengthy requirements documents, which are then translated into technical specifications. UX teams translate them into wireframes. Engineering reviews, creates epics and stories, and asks clarifying questions. Then come the tradeoffs, the misunderstandings, the ambiguity, the missed context — all before a line of code is written.
The process is logical on paper but rarely efficient in practice. By the time something tangible exists, weeks or months have passed — often with misalignment baked in.
With tools like ChatGPT, Replit, and Framer, teams can now move differently. Instead of describing what they want in a doc, product managers can express their intent directly — and AI turns it into a working prototype that behaves like the real thing.
What’s important here isn’t just the speed. It’s the change in where the conversation starts. Rather than reacting to text alone, engineers can click through flows, poke holes, and ask questions rooted in logic and constraints.
The prototype becomes a shared artifact. While it’s directional, it’s enough to get everyone moving.
We saw this in action during a recent redesign of a search experience for a commerce catalog. The product manager knew what customers needed — faster, smarter search with clear filters and compatibility signals — but writing it down felt slow and abstract. The project seemed significantly large, which led to lots of conversation about priority and whether it’s even a thing to be done. Analysis paralysis ensued.
So instead, they used AI to generate a prototype. It wasn’t pixel-perfect, but it showed the flow: the search bar, the filtering logic, the product results with clear tags. It also mocked out where third-party tools would be used versus what would need to be built in-house.
The engineering team saw the core idea instantly. They flagged edge cases, pointed out reusable integration patterns, and scoped feasibility in hours instead of weeks. The result was a shorter discovery cycle, fewer missteps, and clearer priorities.
AI didn’t build the product. But it solved two critical questions before development even began: what to build and how much.
It’s worth saying: no one is pretending AI prototypes are production-grade. Engineers don’t want PMs handing off AI-generated code to merge into the stack. Not yet, anyway.
But AI helps with both clarity and momentum. It makes the abstract concrete. And in many cases, it reveals complexity early, when it’s easier and cheaper to deal with. Instead of starting with documents and ending with rewrites, teams start with the interaction and evolve from there. What’s important here is better alignment.
To see just how far this can go, we partnered with one of our B2B customers to prototype an AI assistant for their buyer experience.
Traditionally, a project like this would’ve started with slides and requirements. Instead, the product manager and engineering lead jumped into a collaborative build session. Using Replit and real user flows, they built a functioning prototype in under 40 minutes.
The assistant allowed users to search using natural language. It could check logic, add items to a cart, and even support bulk quoting. While it wasn’t production-ready, it’s interactive, usable, and grounded in real customer behavior.
Instead of debating hypotheticals, the customer had something they could demo — to internal stakeholders, to sales, even to customers. It sparked immediate, high-quality conversations around data quality, logic, and where AI could meaningfully reduce support overhead.
Right now, vibecoding is primarily a communication tool, but the direction is clear. Prototypes will get smarter. Code reuse will improve. Basic testing and optimization will come built-in. And as a result, product managers will play an increasingly active role in shaping early product experiences, giving engineering a head start.
The goal is making the collaboration between product and engineering faster, sharper, and more grounded. In other words, the roles don’t change, but the relationship does.
Sign up to hear more about commerce, merchandising and development best practices, and our flexible, API-first commerce platform.
Loading Form...
You'll receive a confirmation email shortly.