Intelligent MVP with AI: strategies to launch your product faster and validate it with less risk

The way we create new products is changing — and fast. Before, turning an idea into something testable involved weeks (or months) of design, development, and alignment. Today, with tools like ChatGPT, AI-powered Figma, n8n, Framer, and many others, it’s possible to build rich, functional experiences in a matter of hours.

This new scenario not only accelerates the path to MVP — it changes the very concept of what an MVP is.

At the heart of this transformation is generative artificial intelligence , which not only produces prototypes or interfaces, but simulates interactions, generates copy, organizes data, models user behavior, and learns from real-time feedback . Instead of asking “how are we going to build this?”, the question now is “what hypothesis do we want to validate — and what is the fastest way to do it with quality?”

The MVP as a discovery tool, not as a smaller version of the product

There is still a lot of confusion about what an MVP actually is. It is not a simplified version of the final product. Nor is it a partial delivery. It is a discovery tool , created to test a hypothesis. And that does not always require software.

Some MVPs are a landing page. Others are a manual email. Others are a form, an improvised automation, or a service provided behind a fictitious interface. They all have one thing in common: does this solve someone's real problem, in a way that makes that person engage with the solution?

What has changed with AI — and the low-code ecosystem — is that these questions can now be tested with much more sophistication, and with much less effort.

The more sophisticated the MVP, the greater the need to think about the bridge to the real product.

With this new technical capability, we see increasingly complete MVPs: with fluid experiences, real interfaces, API integrations, automated flows, interactive simulators, and even AI co-pilots. This is excellent — it increases the learning rate and increases the chance of attracting early adopters.

But it also brings a new risk: that of creating a technically complex MVP that cannot be used in the final product.

At this point, a critical architectural decision comes into play: if the MVP is made with very rigid tools or poorly designed temporary solutions, learning will come — but at a high technical cost of refactoring.

On the other hand, if the MVP is built with a minimally solid technical base — even if simple — it can serve as an embryo of the real product , reducing the reconstruction effort and saving time in subsequent versions.

With the advent of AI, the cost of refactoring has also fallen. Code copilots, generative models that write tests, generate documentation, and automate parts of refactoring, are making it more feasible to scrap and refactor . This doesn’t mean we should code lightly, but it does mean that the cost of making mistakes has become less prohibitive.

Still, when the MVP already needs to validate complex journeys, scalability or security, thinking about an evolutionary architecture from the beginning makes all the difference .

AI reduces friction, but does not replace strategy

With all this ability to quickly generate interfaces, texts, flows, and even functional prototypes, it’s easy to get lost in the abundance. But AI, as powerful as it is, doesn’t choose hypotheses. It doesn’t decide what’s worth testing. And it’s no substitute for the strategic clarity of knowing what problem we’re trying to solve.

A smart MVP is not one made with the latest technology. It is one that reveals truths that were previously invisible . And if it can do this with technical quality and future use, even better.

Conclusion

AI is transforming the way we get ideas off the ground. It’s never been easier — or cheaper — to build a working MVP. But that makes it even more important to be intentional: to test what matters, to truly learn, and not just ship quickly.

The more sophisticated the hypothesis, the more careful the engineering behind the MVP must be — not to anticipate the final product, but to ensure that what is validated today can grow tomorrow without needing to be reinvented .

The new Smart MVP combines three elements: strategic clarity, experimental speed, and technical intelligence.

And it is at this intersection that products are born that not only work — but make sense.

References

• Eric Ries (2011). The Lean Startup

• McKinsey (2023). What separates digital product winners from the rest

• CB Insights (2022). Why Startups Fail

• Thoughtworks Radar (2024). The rise of disposable prototypes

• OpenAI (2023). From zero to prototype: the role of GenAI in product development