The Future of Prototyping Products: How AI Can Change the Industry
AI Can be a Game-Changing Influence on Prototyping Products
Creating a prototype is a critical step in product development. It is essential for product designers as it allows us to create something that resembles the final product much more quickly than in code.
This way, the designer can test the product idea during the product design process using a prototyping tool to feel the solution.
In addition, It gives the designers and the product team a chance to test the idea with potential users during the design process to understand if there are any usability issues.
By doing so, the team can experience the concept and verify its effectiveness before releasing it to the public. This allows for refining the idea and ensuring the users find it useful and easy to use.
We have seen changes in how we design products since artificial intelligence emerged, with a big boom in the last six months. More tools are available to assist us in UX writing, brainstorming, and creating visuals. Since we are only in the first steps of this revolution, many things will likely change soon.
With this article, I want to share how I see how we make prototypes in the future and how it will affect our work processes. I also include some examples from real products that might have started this revolution.
It is difficult to make prototypes as real products
Prototypes have always been an essential part of product design because they let us test and evaluate ideas fast with minimal effort without using developers to write all the code.
The idea is simple: connect images with points users can click to move on. This simple technique lets us feel the product before we develop it.
Throughout the years, we’ve improved the prototype to be as realistic as possible (for example, by adding animations). Still, sometimes the technology fails to meet our needs, and creating something real can take a lot of time if you want it.
An example would be when a designer wants to make a prototype look as real as possible, so they connect so many screens that it becomes difficult to manage. Inputs are another example; if you want to make their behavior real, it’s not easy.
Today’s prototyping challenges and how AI can help
The AI revolution has made huge improvements in the last six months, and it looks like it won’t stop. We see new technologies and tools daily that make our work easier and more efficient.
UX writing is a perfect example of product design evolution. In the past, designers spent a lot of time creating text for interfaces. The process involved drafting the copy and ensuring that it aligned with the product’s overall tone of voice, and it also had to fit within the character limit.
This is no longer the case, however. Now, the designer can create a set of prompts that articulate the tone of voice of the product, specify the character count for the message, and outline what they want to say. Using these prompts, the designer can generate clear, consistent product copy suited to the user’s needs much faster.
Prototyping with AI in the future: what can we expect
Here are some options that I believe will help designers in the future to speed up the prototyping process, creating interactive prototypes more easily with the help of AI.
Some of the options I present in this article are already functioning today, and you can test them.
Connect screens automatically
In the future, it’d be great to connect screens automatically with one button in Figma.
For example, you can create a login flow for your new product and provide some context to the AI. (“Here’s a login flow for my app. Make a prototype.”).
The AI can then build the interactive prototype, connecting all the screens automatically and presenting a closer representation of the functionality of the finished product.
This will make your workflow much smoother because you won’t have to connect screens individually. With just one click, you’ll have a prototype.
Create a direct app from Figma more easily, with no prototype, direct code
Creating a product with Figma is an option today, and a few companies are doing this. A simple example is Anima and Bravo, which let you convert a Figma design into a website or app using their plugins.
Buzzy is another plugin that does something similar but incorporates AI as well. The concept is simple: you create a design system UI kit in Figma, then open the plugin and use AI to generate a real product directly from the text.
Of course, you’ll need to modify the results and make adjustments to ensure they fit your needs perfectly. However, the ability to create a product so quickly is fantastic.
Here is a video I did showing its capabilities if you’d like to see it in action.
I believe that more apps will adopt this technique, and in the future, we will witness usability tests being conducted on fully functional products. This means designers can test and validate their ideas faster than ever with a real product and less effort.
An AI machine that will simulate users so that we can estimate errors
One of the possibilities is that we may be able to conduct usability tests without actual users. It sounds strange because how can you validate an idea if you don’t test it with users?
The idea is simple: we will have an AI inside the real product that studies and understands how users interact with the product. Then, the AI will build artificial users based on all its data.
Imagine that you want to change the interface of your product. This type of change can be very critical because people may not understand how to work with it well.
You will be able to create prototypes and then run synthetic users that are built with all the data the AI generates. This will enable you to evaluate better if the users understand the new interface.
Let’s consider another example: A/B testing. AI could automate A/B testing of different design elements within multiple prototypes. This could provide designers with valuable insights into which designs are most effective. Instead of testing it with real users, you will do it on a machine.
I want to say that big data alone cannot solve everything. Testing with people is always necessary because people don’t function like machines. It’s challenging to predict human behavior and how it will interact with the interface, even if you have a lot of big data.
Based on the feedback, the AI will automatically improve the design
Imagine this: you conduct a usability test, gather user insights, and then want to improve the design based on the usability test insights.
You must return to Figma to rebuild the screens and test again. However, with AI, you can return to Figma and input the pain points users experience with the product.
Then the AI will suggest flows and interaction variations based on the design, and you will select what will be more accurate to the user needs.
Then you can create another prototype and test it again.
Auto-generating realistic prototypes from pen and paper is another exciting capability
Imagine sketching a flow for your product idea on paper or your iPad, and the AI comprehends it, creating direct screens and concept prototypes.
This option would save designers time and energy, allowing them to focus on the product’s creativity and overall experience directly from a simple sketch.
Some products, like Uizard, can already scan sketches. However, if companies further develop this technology, we may reach a point where designers can create initial screens directly from their sketches based on the design system. This would be a significant improvement.
Find edge cases in the design
Usually, we use prototypes to experience the design and test them with users. Another thing I like to do with prototypes is to build them to discover edge cases.
The idea is straightforward: I play with the prototypes I created and try to think of and discover edge cases that users may encounter when they use the product.
Now, imagine working with AI in the prototyping process and asking it to analyze any functionality edge cases it can find for us. The AI conducts numerous tests quickly and then presents us with the edge cases that users may encounter, encouraging us to fix them.
We cannot know what we will have in the future
Despite my attempts to explain in this article how I imagine the future of prototyping in product design, I must acknowledge that no one can predict what we will have.
Many challenges are associated with AI, and not all have been solved. For example, it’s biased, and there are instances where it can reinvent the wheel and provide inaccurate information.
Having said that, AI can change many aspects of how we work in product design today. It won’t be long before we use it more and more in our work.
To summarize
In this article, I explored and shared with you the new possibilities artificial intelligence (AI) could bring to product prototyping.
I discussed the limitations of conventional prototyping methods and highlighted AI’s potential to streamline and improve the prototyping process.
As I explored throughout the post, AI could play a significant role in automatically linking screens. I also discussed the transformation of design tools into real products and the ability to conduct usability tests to test your MVP (minimum viable product).
Additionally, I touched on AI’s ability to refine designs based on user feedback and identify potential design edge cases.
Although AI faces challenges and cannot completely replace human designers, it is important to note that AI will enormously impact product prototyping and UX design.
👉 Learn AI for Product Design: Enroll in my Upcoming Course
In this context, I would like to mention that I plan to launch a workshop in the near future showcasing different uses for AI for product designers.
Thank you for reading the article. I hope this article helped you imagine how AI can change how we work with prototypes. Please feel free to share it with your friends or team members, and if you have any questions, please let me know.
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