Design systems are increasingly made to be a set of instructions for AI. How does this change the way they’re built? And what are the efficiency gains for businesses?
Design systems are all about consistency and scale. A shared set of components, guidelines, and principles that anyone in the organisation (be it designers, developers, marketers or content creators) can use to create new things confidently. In other words, a good design system ensures that all new digital touchpoints are built efficiently without messily reinventing the wheel or deviating from the brand guidelines and quality requirements.
With AI, that end goal and the need for design systems remains the same. But the way the design systems are built and how they're used is changing drastically and fast. In this article, we'll get up to speed with the broad strokes of what's happening and how it can benefit your daily operations.
Before, users would seek the required components and instructions directly from the design system. Now the process works so that the user doesn't necessarily interact with the design system at all. Instead, they tell the AI agent what they want to build – and it generates the result in accordance with the rules of the design system. In other words: the AI has already been trained to produce material that matches the brand guidelines with no need for the user to dive through the documentation.
This radically changes the requirements for a design system. Writing guidelines for a human user means you can often rely on them to interpret things without overt handholding or even consult a colleague when something isn't clear. Instructing AI requires a more precise mindset and explanations that leave no room for misinterpretation.
When creating design systems, designers often need to spend quite some time producing variations and pixel-perfect details. This is not the best way to utilise the working hours of a senior designer: many monotonous and time-consuming phases can now be automated.
This means that designers can spend more time on the work that requires expertise of the highest degree: defining the visual language, shaping the user experience and building the strategic foundation. And perhaps most importantly: instructing the AI in detail so it can reliably implement these things.
In many cases, the approach changes the typical workflow where one first designs everything in great detail before moving on to technical implementation. AI-assisted design systems can often produce nearly production-ready technical and visual solutions based on contextual understanding and comprehensive instructions.
The bar for the system underneath the automation is even higher than before. A bad design system produces AI slop at an impressive speed, and fixing it can hurt the efficiency at best or damage the brand's reputation at worst. The most reliable systems are still made by people who can both lay a reliable foundation and communicate its internal logic with precision.
At Taiste, this shift has changed a lot in terms of how we think about building design systems. Structural decisions, naming conventions, documentation and guidelines all now need to take both human and AI users into account. This kind of systems thinking requires experience with how AI tools interpret the commands.
The upside for clients is significant: faster output with consistent results, and a design team that can focus their time and energy on understanding and creating the vision and principles of the products.
These changes are so fundamental that a design system that was state of the art three years ago may not be the right foundation for staying competitive anymore. Now is the perfect time to ask where yours is.
Are you new to design systems? Check out our Design Systems Guide and get yourself familiar with the topic in no time.
