UX/UI Design and AI: designing digital “simplicity”

Professionals working in UX/UI design face a multidisciplinary challenge every day: from qualitative research to data interpretation, from defining experience flows to refining interfaces, usability, and accessibility.

In this context, tools have undergone a radical evolution. Figma, now the industry standard, has transformed workflows from a constellation of fragmented software into a single integrated environment. Today, within one space, it is possible to conduct analysis, map customer journeys, build design systems, and produce interactive prototypes—making the dispersion across multiple tools a nostalgic memory. Artificial intelligence has recently entered this ecosystem, initially through plug-ins and now through native, prompt-based functionalities.

A new operational synergy

Solutions such as Figma Make, designed to extend the designer’s scope by integrating with Figma and Figma Sites, enable the conversion of creative visions into actual applications, shortening the distance between ideation and technical implementation. This is not about replacing development skills or eliminating collaboration with programmers, but about understanding how to work alongside what is, in effect, a new “colleague”. This digital entity acts as both cognitive and operational support, influencing the entire creative cycle—far beyond simply increasing execution speed.

The integration of industry tools with AI agents such as ChatGPT, Gemini, Claude, Copilot - or emerging platforms like Google Stitch - represents an essential alliance in addressing the most significant daily challenge: translating Alain Berthoz’s concept of “simplexity” into the design of physical and digital products and services. This refers to the innate ability of living beings to manage complexity through elegant, effective strategies—simple without being simplistic.

Impact on daily work

Working alongside an artificial collaborator—knowledgeable, fast, and capable of processing stimuli—can transform the trajectory of an idea, much like collaborating with a trusted human colleague. But how does this work in practice? AI-integrated tools enhance user research, automate repetitive data analysis tasks, structure accurate workflow hypotheses, and reduce informational noise—accelerating the transition from evidence to strategic decision-making.

In the visual phase, AI enables the generation of interfaces from textual instructions, references, or functional constraints. This approach, known as prompt-to-UI, significantly reduces initial inertia and provides a strong boost to exploration, freeing up valuable time for critical evaluation of proposed solutions. More recently, the opening of design tools to AI agents has enabled the training of systems that, through sets of “skills” (chained instructions), can act directly on files, making the refinement process even more seamless.

More design for everyone

As in any effective collaboration, the quality of the outcome in working with artificial intelligence depends on the quality of communication. Using these advanced models first requires the ability to formulate effective requests: defining objectives, context, constraints, and expected outcomes. In other words, it means designing an interaction.

For this reason, UX skills become central even in dialogue with generative systems: those who can design user experiences can also better guide AI responses. Conversely, the widespread adoption of these tools is democratizing the culture of interaction design. Increasingly, people are engaging with a fundamentally UX question: what actions or instructions should I formulate to obtain what I want? A process already initiated by platforms such as Canva, which have made practices once reserved for professionals widely accessible.

This transformation is also reflected in how digital products are designed. For years, UX/UI has largely relied on a logic of information exposure: menus, pages, categories, sections, and navigation paths designed to be explored by users. Today, however, people are increasingly accustomed to expressing requests or intentions, expecting systems to retrieve, organize, and generate the most relevant response.

As a result, the focus shifts to making it clear what can be asked, how to ask it, what the system’s capabilities and limitations are, and how to manage potential errors or misunderstandings.

Inclusive interactions

This approach finds vital application in accessibility. Designing for accessibility today means increasingly questioning how a system can be queried, listened to, or controlled by as many people as possible, across diverse usage conditions. It means creating systems that function for a plurality of bodies, senses, cognitive and motor abilities, devices, and environmental contexts.

By its nature, AI contributes to the creation of products capable of interpreting non-standard inputs, handling hesitation or incomplete requests, providing clear and progressive responses, enabling corrections, anticipating alternative paths, and avoiding reliance on a single interaction mode. In other words, it helps make products not only more accessible in regulatory terms, but more flexible and inclusive in everyday use.

Design to code

The integration of AI into design tools - from Figma Dev Mode to Figma Make, Figma Sites, and dedicated plugins - is making the design-to-code paradigm increasingly tangible, reducing the gap between design and development. Designers can now produce outputs that are semantically coherent and closer to final implementation, up to the generation of markup and technical documentation.

Projects leaving User/Customer Experience teams are now hybrid outputs with a semi-executable nature. Clearer specifications, consistent naming, and better formalized logic make the handoff to development smoother, reducing ambiguity and improving collaboration between designers and developers - with tangible benefits for clients and end users.

From UX/UI Designer to…

With the integrated adoption of AI in design tools, UX/UI designers can now more easily govern the overall quality of the experience, operating on two levels: on one hand designing experiences for people, and on the other interacting with intelligent systems that are part of both the design process and the final product.

As a result, their role evolves into a more strategic and systemic position - closer to the actual functioning of products and better equipped to design ecosystems centered on the relationship between people, technologies, and usage contexts.