AI as Your Prototyping Partner - Reduce Costs and Speed Up Innovation
Depending on your position, you may be thinking about how to make AI your business partner, or you might still consider it the elephant in the room. When it comes to prototyping, AI is more than just a tool – it accelerates iteration, unlocks creativity, and makes the process more cost-effective.
In this article, I’ll show how AI can streamline prototyping, reduce costs, and speed up innovation. I’ll present the most effective AI-driven tools and share practical strategies to help you iterate faster and bring ideas to life more efficiently.
How to Start?
Adopting AI within a company begins with a shift in mindset. Many employees may view AI as a threat – something that could replace their roles or add unnecessary complexity. However, the reality is quite the opposite. AI tools are designed to assist, not replace, and to make work easier, more efficient, and ultimately more rewarding.
Introducing AI tools and fostering a mindset change within a company is a strategic challenge, but proven frameworks and methodologies can help smooth the process. If you need help implementing such changes in your company, consider exploring structured approaches like Kotter’s 8-Step Change Model or the ADKAR Model.
Organizations need to foster acceptance and curiosity by initiating discussions among leaders to drive transformation and position AI as a tool for productivity. Highlighting how AI simplifies tasks and improves efficiency can inspire teams. Demonstrating automation tools and showcasing their impact through focused meetings is key. Our experience at Exlabs has shown that meetings alone are not enough – a clear plan, progress tracking, and readiness to answer questions are essential for a successful transformation. This structured approach ensures smoother implementation and better results.
The Role of AI in Rapid Prototyping
In our industry, prototyping is key. It allows teams to quickly validate initial ideas, opens the door for further improvements, and enables the collection of valuable feedback at an early stage. Not long ago, creating prototypes was reserved for highly technical individuals. Today, this is changing, and even less technical professionals who are familiar with the area can use prompts to create initial prototypes.
From my observations, advancing a prototype or creating something more sophisticated requires programming knowledge. This expertise highlights critical factors that less experienced individuals might overlook. Such an approach helps avoid blind spots or errors that are difficult to correct later. However, if you’re looking to build something simple, AI-powered tools can often help you create a first version without any prior experience.
The ability to create tangible, high-fidelity prototypes within hours can make or break a project. There are AI tools that empower teams to move from concept to working model at unprecedented speeds. Whether it’s a clickable app demo or a functional UI prototype, these tools allow us to visualize and validate ideas quickly, identifying flaws or missed opportunities before committing significant resources. Prototyping with AI isn’t just about speed; it’s about risk mitigation. This early-stage prototyping aligns cross-functional teams with a clear vision.
Creating Functional Prototypes
One of the key advantages of modern tools is the ability to migrate Figma designs into modern applications. The entire migration process takes just a few minutes. While not perfect, the results are surprisingly effective and allow for customization of the architecture and libraries used.
Over the past months, I have tested several tools and noticed their rapid evolution. Some that were barely functional a while ago are now integral to our daily workflow, showcasing how quickly this technology is advancing.
Locofy.ai
This tool allows projects to be migrated from Figma to React, Angular, or Vue in just a few minutes. It offers the flexibility to select libraries like Material UI, Ant Design, or Chakra UI, aligning with your specific design and development needs. By automating these transitions, you can significantly reduce development time and operational costs. Locofy features case studies on its website showcasing a time savings of up to 50% in development time. The tool handles even more demanding designs, including shadows under elements, gradients, and complex background patterns. In my tests, it proved to be one of the best tools for migrating from Figma to functional prototypes. A very useful feature is the wide range of styling options available. You can choose between CSS Modules, Tailwind, Sass Modules, Styled Components, and more.
V0 from Vercel
V0 is a tool from a well-known company that is behind, among others, Next.js. Prototyping with command-side logic is solid, and visuals have been greatly improved. It’s worth noting that V0 has expanded greatly in recent times. A while back, the prototypes I could generate looked very basic, but now the improvements are clear, showing the substantial effort Vercel has invested. Regarding migrating from Figma to React this functionality is reserved for higher-tier plans, so you cannot test it without a subscription. In contrast, competitors often provide free trials for similar features.
Sometimes, there are issues when requesting the use of a specific library. The prototype might fail to build initially, requiring manual adjustments to guide it. I wasn’t able to use different styling approach or use different UI library than ShadCN. I haven’t encountered such challenges on this scale in other applications.
Bolt.new
Bolt is another interesting tool that allows you to generate prototypes in multiple frameworks and libraries such as Angular, Astro, Next.js, or Vue. In my opinion, it is one of the best tools for such tasks, performing exceptionally well with more challenging scenarios. Compared to Lovable (which will be discussed shortly), it sometimes handles context and advanced UI-specific aspects better. Both Bolt and Lovable offer integration with Supabase, enabling the creation of simple backends. One downside I noticed while working with Bolt was the rollback functionality. While Bolt allows reverting to a specific point, its history is quite limited. If you’re exploring different approaches and later decide to revert, it won’t be a trivial process.
It’s worth mentioning that this solution also allows for creating a mobile app prototype using Expo.
Lovable
In my opinion, it deserves a spot on the podium alongside Bolt. It’s an excellent tool that, while occasionally performing worse in UI aspects compared to Bolt, excels in backend prototyping (leveraging Supabase and edge functions). Officially, Lovable builds frontends using React, Tailwind, and Vite. Interestingly, even when I requested a migration from Tailwind to another solution, it worked successfully. A noteworthy feature is the “history” option, which allows you to revert to a previous state when AI introduces errors that are difficult to undo.
Keep in mind that several of the tools discussed here go beyond front-end prototyping to include back-end logic creation. This capability for full-stack prototyping offers significant advantages, enabling you to validate complex scenarios efficiently and reduce the time to actionable insights.
AI-Driven Development Efficiency
It’s worth mentioning that beyond optimizing prototyping processes, accelerating standard software development is equally important – especially when you bring a generated prototype into your repository and need to navigate the code or make adjustments that mentioned AI tools couldn’t handle. Tools like GitHub Copilot automate repetitive tasks, generate boilerplate code, and, most importantly, help reduce the cost of debugging, testing, and understanding the code generated by these tools.
At Exlabs, we found that experimenting with different models worked well for us, and we recommend trying it with your own projects to see which model fits best for your needs. In the settings, you can select the preferred model, such as GPT-4o or Claude 3.7 Sonnet, to optimize performance for specific tasks.
Why Technical Knowledge Is Still Crucial
Through my evaluation of various tools, I reached an important conclusion: the person operating these tools should have a strong technical foundation, ideally as a full-stack developer. Why? Often, AI would get confused or fix one issue while breaking something else. This was especially common in the advanced stages of prototyping, such as fine-tuning functionalities, where AI struggled to pinpoint errors. In these situations, knowing the application inside and out allowed me to identify where the problem likely resided and how to resolve it. With the right prompt, AI would get back on track.
Take aways
Try Bolt.new if you prioritize a refined UI. The application performs well even with advanced aspects, as long as it is guided effectively.
Explore Lovable, which, while slightly less effective in UI tasks compared to Bolt, compensates with other strengths, making it well worth testing.
If you want to try migrating from Figma to code, use Locofy. You’ll be impressed by the results.
Remember, implementing changes in a company is a process that requires careful management. One or two meetings about introducing AI tools won’t suffice.
Don’t forget to leverage tools for traditional software development. They excel as consultants for debugging or improving tests.
Remember, the question isn’t whether to adopt AI – it’s how quickly you can start benefiting from its potential. Challenge Your Status Quo and imagine being able to validate an idea in days instead of months. That’s the reality we’ve experienced.
Even if you’re not fully satisfied with AI’s efficiency today, integrating these processes into your company is still a strategic move. The AI we know today is the least advanced it will ever be – every iteration will only make it better. By establishing structured workflows around AI now, you’ll not only enhance your company’s efficiency today but also future-proof it for the innovations to come.