When I started at WeSalute, I was the only designer. One person. One set of eyes. One brain trying to hold the entire visual and product language of a company together while simultaneously shipping work every week.
If you've been in that seat, you know what it feels like. There's no one to pressure-test your decisions. No design review where a colleague catches the thing you've been staring at too long to see anymore. No shared system to pull from because you're the one who's supposed to build it, somewhere between all the other things that are also your job.
Building a design system as a solo designer is one of those tasks that always feels important and rarely feels urgent. Something else always has a stronger claim on your time. And so the system either never gets built or it gets built in pieces over years, inconsistently, in the margins of other work.
AI changed that calculus for me in a real way. Not by doing the design work, but by handling enough of the surrounding work that the design work became more possible. Here's how I approached it and what I'd do the same way again.
Start with the audit you've been avoiding
Every design system starts the same way, with an honest accounting of what actually exists. What colors are you using and are they consistent? How many variations of the same button have you shipped over the years? What does your type scale actually look like when you pull it all into one place?
This is the work that's important but deeply unsexy, and it's exactly the kind of work AI handles well.
I fed our existing product into an AI-assisted audit process, pulling screenshots, component inventories, and style references into a structured review. Instead of spending days cataloging inconsistencies manually, I could have a conversation with the output, ask questions about patterns, and get to the real design decisions faster.
[Screenshot: audit process or component inventory example]
The audit surfaces the decisions you've been making unconsciously and forces you to make them consciously. AI doesn't make those decisions for you, but it gets you to the decision point without the exhausting manual work that precedes it.
Use AI to pressure-test your decisions
One of the real costs of working alone is that your decisions don't get challenged. You might have a strong instinct about a spacing system or a color ramp, but without someone to push back, you never find out if it actually holds up under scrutiny.
I started using AI as a kind of design review stand-in. Not to validate my choices, but to stress test them. I'd describe a decision I was making, give the context behind it, and ask for the strongest case against it. Sometimes the counterargument was weak and I moved forward with more confidence. Sometimes it surfaced something I hadn't considered and I went back to the drawing board.
[Example: prompt and response showing a challenged design decision]
This isn't the same as a human design review. It's missing the intuition, the shared context, the relationship. But for a solo designer who doesn't have that human review available, it's a meaningful substitute for the kind of thinking that catches blind spots.
Build the documentation in the same breath as the design
The part of design systems that usually dies first when you're under-resourced is the documentation. You build the components, you ship the system, and then the documentation becomes a permanent item on a to-do list that never quite gets done.
The pattern I developed was to document while designing rather than after. At each decision point, I'd write down the reasoning in plain language and then use AI to help shape it into proper documentation. The thinking was already in my head, I just needed a process that didn't make capturing it feel like a separate project.
[Example: design decision note transformed into documentation]
The result is a system that explains itself. When someone joins the team or a stakeholder asks why something works the way it does, the answer exists somewhere and it's actually readable.
Token systems are where AI really earns it
If you've tried to build a token system alone, you know how quickly it becomes its own full-time job. Naming conventions, hierarchies, semantic vs. reference tokens, dark mode considerations, platform-specific variations. It's genuinely complex and the cost of getting it wrong is high because everything downstream depends on it.
This is where I found AI most useful as a true thinking partner rather than just an execution aid. I could describe the structure I was imagining, talk through the tradeoffs, ask about edge cases, and build toward a system that was actually reasoned through rather than just good enough.
[Example: token naming conversation and resulting structure]
[Screenshot: final token system or Figma variables setup]
The system still reflects my decisions. But those decisions were better because I wasn't making them alone.
The component logic problem
Solo designers often end up with component libraries that are technically complete but practically fragile. Everything works until it doesn't, usually because the variants weren't fully thought through or the props weren't structured in a way that scales.
I used AI to walk through component logic before building in Figma. Describe the component, describe all the states and variants you think you need, and then have a conversation about what's missing. Edge cases. Accessibility states. What happens when the content is longer than expected. What the mobile behavior should be.
[Example: component logic conversation]
[Screenshot: resulting component structure in Figma]
By the time I was actually building in Figma, the thinking was done. I wasn't discovering problems in the tool, I was just executing decisions I'd already made.
What this actually saved me
I want to be specific here because vague claims about AI saving time are everywhere and mostly useless.
The things that actually got faster were the ones that used to require either another person or a separate extended focus session that I didn't always have. The audit. The documentation. The token naming. The component logic review. Each of those individually might represent days of work spread across weeks. Compressed, they represent a design system that actually got finished.
[Before/after timeline or process comparison]
What didn't get faster was the actual design judgment. Deciding what the system should feel like. Making the calls about color and type and spacing that give the whole thing its personality. That's still on me and it should be. AI can tell you how to structure a token system. It can't tell you whether your brand should feel warm or clinical, confident or approachable. That's the designer's job.
What I'd tell another solo designer
Start smaller than you think you need to. A design system doesn't need to be comprehensive on day one. It needs to be real, which means it needs to be used. Pick the five components you ship most often and build those properly before touching anything else.
Use AI for the work that's blocking you from doing the design work. Auditing. Documenting. Naming. Structuring. Let it compress the overhead so more of your time goes to the decisions that actually require your eye.
And don't confuse a fast design system with a good one. AI will help you build it faster. Whether it's good still depends entirely on the quality of the thinking you bring to it.
That part hasn't been automated yet. Thankfully.
What did you think?
Share with friends