UX Caffè
AI Can Design an Interface. But Can It Really Design?
Generating a UI now takes only a few minutes. Understanding which problem is worth solving is still a completely different matter.
Artificial intelligence has become an integral part of a designer’s work. Until a few years ago, it was mainly used to generate text, images, or ideas; today, however, it can directly support the process of designing a digital product. We can describe what we want in just a few lines and receive a first interface proposal, a navigable prototype, a responsive structure, or even a working version of the product. Tools such as Figma Make already allow us to turn natural language into layouts, interactions, and editable code, dramatically reducing the distance between an idea and something we can actually see and use.
What used to require several hours of work can now be produced in just a few minutes. So the question comes almost naturally: if artificial intelligence is now capable of designing an interface, how much do we still need a designer? Perhaps, however, that is the wrong question, because creating an interface and designing an experience are not the same thing. And this difference becomes increasingly important as generative tools become more powerful.
Generating does not mean designing
One of the most impressive features of generative tools is their ability to produce visually credible interfaces. We can ask for a dashboard and get a sidebar, a few KPIs, charts, filters, and a table. We can ask for an e-commerce platform and see a product grid, purchase buttons, filters, and a shopping cart. We can describe a mobile application and receive screens that already look ready to be shown to a client. At first glance, everything works: consistent typography, correct spacing, tidy components, and an apparently clear visual hierarchy.
But a visually good interface is not necessarily a good user experience. Design does not begin when we open Figma and start drawing: it begins when we try to understand who will use the product, what problem they need to solve, why someone should use it, which information is really necessary, and what difficulties may emerge during the experience. The screen is only the visible part of all these decisions. Artificial intelligence can dramatically accelerate the production of that screen, but the speed at which it generates a solution does not guarantee that the solution is the right one.
The risk of interfaces that look correct
AI works particularly well with existing patterns. It is familiar with countless dashboards, landing pages, mobile applications, e-commerce platforms, and management systems, which makes it very effective at recognising recurring structures and combining them. This is precisely why an automatically generated interface can immediately feel familiar and, as a result, correct.
The problem is that during a real UX Design process, the question should not only be: “How can we build this screen?”. First, we should ask: “Do we really need this screen?”.
User research could lead us to remove a feature instead of adding one. A test could show us that a five-step process can be reduced to two. We may discover that a visually elegant solution creates confusion, or that information we considered extremely important is of little interest to the people who will actually use the product. Artificial intelligence can be extremely fast at finding an answer, but if the initial problem has been defined incorrectly, we will simply get a very fast answer to the wrong question.
Where AI can really help the designer
Saying that artificial intelligence cannot completely replace the design process does not mean ignoring its enormous potential. On the contrary, AI can become one of the most useful tools in a designer’s daily work. The difference lies in how it is used: not as a shortcut to avoid designing, but as an accelerator within an already thoughtful process.
1. Exploring more alternatives
One of the most obvious advantages is the ability to explore different design directions in much less time. We can change the structure of a screen, compare different visual hierarchies, rearrange information, or quickly generate multiple versions of the same flow. In this case, speed is not simply useful for producing more; it allows us to test more hypotheses before choosing the final solution.
2. Prototyping earlier
Another advantage concerns prototyping. Figma Make, for example, allows us to start from natural language and arrive at interactive experiences that can then be further refined. This reduces the distance between an idea and something we can actually test.
The faster we can turn an assumption into something concrete, the sooner we can validate it with a user, a stakeholder, or a development team, avoiding spending too much time on a solution that may turn out to be wrong.
3. Reducing repetitive work
AI can also accelerate activities such as creating variations, producing temporary content, generating microcopy, building initial layout structures, and handling several operational tasks. The real advantage, however, should not be filling a Figma file with dozens of screens more quickly. It should be freeing up time for design decisions that require greater attention.
The designer’s value is shifting
For a long time, a large part of the designer’s work was inevitably connected to production. Designing screens, building components, preparing variations, creating prototypes, adapting layouts to different resolutions, and documenting interface behaviours required a significant amount of time. Today, many of these activities can be accelerated, and this could make it seem as though the designer’s role is becoming less important.
The data, however, tells a different story. In its 2026 AI Report, Figma found that 90% of respondents consider design at least as important as it was before the arrival of artificial intelligence, while almost six out of ten believe it has become even more important. The reason is quite simple: when producing something becomes easy, understanding what is actually worth producing becomes much more important.
The ability to create a screen quickly will therefore continue to be useful, but it may no longer be the skill that truly makes the difference. The ability to understand a problem, identify real user needs, evaluate alternatives, recognise a weak solution, manage technical and business constraints, and make thoughtful design decisions will become increasingly important.
An artificial intelligence system can suggest ten alternatives in a few seconds. Someone still needs to understand which of those ten actually makes sense or, if necessary, realise that none of them truly solves the problem.
More speed does not necessarily mean more quality
The ability to produce quickly also comes with a less obvious risk: when creating becomes easy, creating too much becomes easy as well. More screens, more features, more variations, and more ideas do not automatically mean a better product.
Generating ten solutions in a few minutes can be extremely useful, but only if we have clear criteria to compare them. Otherwise, we risk choosing the one we like most aesthetically rather than the one that works best for the user.
There is also a problem of perception. A highly polished interface immediately communicates a sense of completeness, and when a screen already looks finished, it becomes harder to question it. Stakeholders may focus on colours, images, or visual details and forget to ask whether the flow actually makes sense.
This is a problem that has always existed in design, but generative tools could amplify it because they make it possible to reach high-fidelity results very early in the process. Looking finished and actually working are still two completely different things.
The questions artificial intelligence does not eliminate
Tools are changing at an impressive speed, while the fundamental questions of UX remain surprisingly similar:
- Who will use this product?
- What is their main goal?
- Which information is really necessary?
- Can we remove any steps?
- Is the solution understandable without explanation?
- What happens when something goes wrong?
- Is the interface accessible?
- Is the product behaviour predictable?
- Are we really solving the original problem?
Artificial intelligence can help us explore possible answers, but it does not make these questions less important. If anything, the opposite is true: the more powerful the tools become at producing solutions, the more important it is to define the problem correctly.
A very detailed prompt can improve the generated result, but the quality of that prompt still depends on our understanding of the product, the user, and the context.
The designer may spend less time drawing
It may sound paradoxical, but the designer of the future could spend less time drawing screens and more time understanding which screens should exist.
The boundary between design and development is already becoming thinner. According to data reported by Figma in its 2026 report, the percentage of designers also involved in development increased from 21% to 41%.
Tools are therefore bringing together disciplines that, for a long time, remained clearly separated. A designer does not necessarily need to become a developer, but they will probably need to understand more and more about how the products they design actually work.
Components, responsive logic, states, data, design systems, and technical constraints will increasingly become part of the design process. In the same way, artificial intelligence will allow designers to get much closer to the final product.
Competing with a machine on how quickly it can produce a screen would therefore make little sense. The designer’s job will be different: understanding when that screen is correct, when it needs to be changed, and above all when it should not exist at all.
Conclusions
Artificial intelligence can already create convincing interfaces, and in the coming years it will certainly become even more effective. The speed at which these tools are evolving is impressive: today, we can start from a simple text description and arrive in just a few minutes at an interactive UI, a working prototype, or even a first version of the code. It would therefore be naive to think that all of this will not change the designer’s work. It will change it profoundly, because many operational activities will require less time, while the creation of variations, the exploration of alternatives, prototyping, and part of the production process can be delegated or significantly accelerated.
This does not mean, however, that designing will become less important. The exact opposite may happen. When almost anyone can quickly generate a professional-looking screen, the ability to understand which screen actually makes sense becomes even more valuable. Design is not only what we see on the screen: it is the result of a series of decisions made before that screen is drawn. Understanding people, defining the problem correctly, setting priorities, removing what is unnecessary, managing trade-offs, and verifying that a solution truly works remain central activities in the process.
Artificial intelligence can therefore become an extremely powerful collaborator, especially if it allows us to eliminate part of the repetitive work and use that time for research, analysis, and validation. The risk appears when we confuse its ability to produce with the ability to design, automatically considering a solution valid simply because it looks complete and professional.
A beautiful interface can be generated in a few seconds. A good experience still requires understanding, context, and judgement. And this is probably where the future of the designer’s work lies: not in trying to design faster than artificial intelligence, but in using that speed to spend more time on what really matters. Understanding which problem we are solving, who we are solving it for, which trade-offs we are willing to accept, and whether the solution we are building is actually worth creating.
The better AI becomes at answering our requests, the more important it will become for us to get better at asking the right questions.
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