August 8, 2025
Nobody knows exactly where all this is going, and that’s the nice part …
Last week, as part of my bi-monthly Rein-Tow, the question, I gathered with a group of very thoughtful design system practitioners to talk about the role of Ai In our design system programs. This was not a hype session, it was a real conversation based on data from 74 of you who responded to three simple questions:
- How do you feel about your future in design systems, given the current state of AI?
- Why do you feel that way?
- How is it Actually usage Ai Work today to support your design system?
TLDR: Ai Certainly changes our work, but not in the way in which leadership often accepts (or even wants).
Carefully optimistic
I have been part of a few conversations about Ai In design systems and they usually seem to live at one end of a spectrum. Some are filled with existential fear, others feel as a fanboy twinding. Some are aimed at how Ai Will replace us while others are about how much more we can achieve. When we all asked you how you felt about this, you came back with an average rating of 3.3 on a zero to five points scale. That may not sound exciting, but it feels a clear improvement of earlier conversations I have had.
When asked how they feel about their future in design systems in view of the current state of AI, most respondents were on the optimistic side of the scale.
The spectrum of feelings was well represented by these two responses:
‘I feel that someone takes the carpet under me. ‘
‘I think there has never been a more exciting time for us Ds practitioners. “
After reading all your answers, the way I would describe our combined sentiment, ‘Carefully optimistic. ‘
Leadership wants magic
One of the most consistent themes in the discussion was the separation between practitioners and leadership. People from Dell, Target, Spotify and elsewhere shared stories about leaders who insist on ways to integrate Ai to stay behind in workflows and products for fear. Given the hype, this is an understandable feeling.
Daniel shared in the deep dive:
‘We started with one Ai Chatbot for support and leadership thought it would solve all our problems. In reality it only answers the most basic questions and we still need a real team. “
July reflected this sentiment and shared how she saw striking Ai Examples really shift the story from leadership. The challenge is to help our leaders understand the gap between those examples and really useful changes in our workflows.
Brandon shared that he was successful to help his leaders understand this gap by communicating that there is a lot of work to do to take these examples, identify the successful patterns and bring them to our workflows so that we can improve our results.
That of course adds even more to our plates. Sarah expressed this concisely:
‘Most design system teams cannot even do what they have to do with the people they have, let alone to develop smartly Ai Functionality. It feels like we are entering water. “
There were many nodding heads when we discussed how leadership is often fascinated by the shiny output: atmosphere-coded prototypes, AI-generated slides, code spun in seconds. But those outputs often miss what is most important, Products that are really ready to operate a diverse user base.
The real value is not the output
All this talk about how Ai Being observed by our leaders sounded known to some of us who have been to the industry for a while. Elyse first proclaimed this:
‘Leadership simply behaves the same as they always have. Export is cheap and has always been – we make boxes on screens. The output is not what we should offer there. The value is in the strategy, process and decision -making. “
She called on us to realize that we should be smart about what we choose to spend on Ai. We have to do the work to understand where people shine and where the robots shine.
I spent a challenge on this. As a major computer science that chose Ai As my specialization in Undergrad, I remember that I followed physiological psychology lessons that were part of the artificial intelligence rack. This is a branch of science that focuses on understanding how the brain and nervous system influence our thoughts, feelings and actions. The reason they had let us study the brain is because Ai Is modeled on the brain. My challenge was this: Certainly, leadership behaves as they have always done, but maybe Ai Is different. As the intention behind it Ai For an algorithm for human reason and decision -making, maybe these tools are those who are actually actually can replace us?
Kevin responded quickly by saying that he doesn’t see it Ai As just another tool. Instead, he compared it with the invention of the factory. Just as the time before the industrial revolution was more about handmade goods, perhaps where we are now, about the abyss of a fully transformed society through the hands of Ai.
A greater mentality shift
This philosophical debate was covered by Sandra with the request to propose a future that is very different from our current daily day:
‘We can’t really imagine what Ai Can actually do for us. It is a larger mindset shift. The future may not have visible Onion Not at all. “
Elyse continued with this line of thinking:
‘What if components were effective few instructions? We can just the Onion Every time we had to change something, it would be easy to refact everywhere. ‘
Others came in about how on-demand interfaces and short -lived Onion are already there. And this thread of the deep dive brought us back to the idea that …
Design systems are inputs, no outputs
We have long talked about design systems as a source of truth or agreement. But in an AI-based workflow, the same systems become the ingredients. In other words, the best use of Ai In design systems, it is not to write the documentation or make the components, it is to Ai of Those things.
This thinking offers a nice reaction to leaders who chase the shiny new thing. Perhaps if we position our design systems as context engines for AI-driven product creation, we can show how It’s the combination of Ai and design systems that are the future of our products.
So what does our role look like when this is the future?
Relaxing roles again, again
There is a growing feeling that Ai Changes role in ways that are not always comfortable. Some felt a bit territorial or insecure, others were stimulated by the possibilities.
Caroline summarized it:
‘I’m not afraid of Ai Take my job or become irrelevant. I am afraid it will change my job in such a way that it takes away what I think – at work at work. “
Kevin reformulated the chance:
‘We have to think about the end product to thinking about prompts, models and data sets. That is where the value will be. “
In preparation for the deep dive, I had already synthesized three important ideas for how our roles should shift in this new world.
Three things we can now start to prepare for this brave new world: 1) Position -design systems such as AI training infrastructure, 2) evolve from component makers to system strategists, and 3) develop new skills that are needed to manage and use AI tooling.
How you prepare
- Place the design system as Ai Training infrastructure
- Evolve from a component maker to a system strategist
- Develop new skills in Ai Tool management and quality
In the near future this also means that we can do the work to consider how our product organizations should use Ai responsible. Caroline shared An example of a part of the hard work she and her team have done to document this. Offering this kind of clarity to our teams helps to create a sense of governance and stability when the technology feels as if it is flying by.
From experiments to infrastructure
As a community of practice, there is agreement that we are in the messy center. Most of you who answered the question this week will make a number of experimentation. And most of you also shared that you are struggling to make those experiments valuable in real-world use cases.
Daniel summarized it and said:
‘For POCs and prototypes, Ai Is really great. For end products … meh. “
But there were a few people who have succeeded in taking those experiments in infrastructure. Davy had some advice for this scenario:
‘Be the target. Make different agents for different knowledge domains. Index common questions. Do not only build something to the Ai box.”
And when you find valuable approaches, some people are even source controls instructions to keep things consistent. In general it felt as if our conversation went away from ‘Do we have to use AI? “In the direction of ‘How do we make it Ai Work for us? “
Look forward
Naturally, Ai Get better and better – the models will evolve. But the biggest shifts do not come from new tools, they come from how we to elect To use them.
If your definition of a design system is wide enough, your design system is uniquely positioned here. They are not only repositories of components, they are cultural artifacts and frameworks to think. In an AI driven world, that context is more valuable than ever.
If you want to explore this further, The full data set is here And The partnership notes from our deep diving conversation are here.
A short word of thanks to Omlet for making this episode possible. A lot of work is needed to perform these sessions, both for me and for our Co -Gastheren. Allowing an organization to support this research and our community is useful. If you want better visibility in how your design system is actually used, the standard standard for design system analyzes will be used.
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Curious how others do this? Come and hang with us Redwoods. It is a space for people who want to support each other on the journey of building better design system programs.
Learning mode
I am constantly inspired by the people who participate week after week to dive into the answers we collect. Each of you appears In learning mode. That is why we all walk away with widened perspectives and an appreciation for the experiences that we all bring to these conversations.
To those of you who were present, thanks for participating in such a graceful attitude.
Sources
Thank you
Many thanks to everyone who participated.
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