From individual screens to reusable systems.
AI didn’t change my product thinking. It changed where I spend my time — moving away from manually producing screens, and towards building reusable systems that let me explore, validate and ship faster, without lowering the bar on evidence.
This isn’t a showcase of tools or prompts. It’s a working record of a shift I’ve made in how I practise product design — still evolving, and grounded in real projects rather than demos.
Discovery and research were never the problem. Production was the expensive part.
Traditional product design still leans heavily on manual production — rebuilding layouts, populating dummy data, remaking common patterns, and preparing handover artefacts before engineering can even begin. None of that is thinking. It’s overhead. The diagram below isn’t about replacing Figma, replacing designers, or replacing code — it’s about where the weight of the work sits.
Discovery, research and product thinking already took up most of my time. Production was simply the most expensive part of the process.
The thinking looks almost the same — if anything, it gets a little more room. Production costs a fraction as much, so prototyping and validation take up the space it used to.
AI didn’t change my design process. It changed the economics of it.
- Problem or opportunity
- How much uncertainty exists? Understand & assess
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High uncertainty Discovery & Research Reduce uncertainty before investing.Low uncertainty Reuse & AI-assisted exploration Reuse proven patterns. Accelerate confidently.
- Prototyping Explore, validate and refine ideas rapidly using reusable systems and AI assistance.
- Validate & refine Test with users, gather evidence and build confidence before delivery.
- Engineering Deliver developer-ready solutions with clarity and confidence.
I don’t apply the same process to every problem. Before anything else, I assess how much uncertainty exists. Genuinely uncertain problems still begin with discovery, research and synthesis. Well-understood problems can move more quickly through reusable patterns and AI-assisted exploration. Both paths reconnect in the Playground, where I rapidly prototype, validate and refine ideas before handing over to engineering with greater confidence. For the simplest, lowest-risk fixes, that same assessment can send work straight to engineering — skipping design and discovery altogether. AI hasn’t changed how I make design decisions—it’s changed how quickly I can turn those decisions into something real.
Reusable components became reusable intelligence.
Design system, code editor, prototype and documentation used to live in separate tools, each rebuilt by hand for every project. Inside the Playground, they’re one connected system — components, tokens, accessibility rules and interaction patterns already exist, so instead of asking AI to invent an interface, I ask it to compose one from what’s already proven to work — often pairing a short prompt with a visual reference rather than a text description alone, so the output matches the system by construction, not by chance.
One shift mattered more than I expected: realistic data. Rather than filling screens with lorem ipsum, I built reusable, fictional datasets — invented retailers, products and customers, modelled on real operational patterns — that populate prototypes automatically. A promotional concept tested against a fictional World Cup campaign and invented products felt just as real to participants as a live product, without a single piece of commercial data leaving the building.
There’s a commercial argument here too, not just a craft one. Guessing at an interface from scratch costs more than composing one — more generations, more correction cycles, more tokens spent arriving at the same place. Reusing a proven system gets there in fewer steps, which means a lower AI bill for the same outcome, not just a faster one.
Where this actually happened.
Scaling a mapping experience without redrawing it by hand.
A location-based homepage experience needed to work across multiple products and regions. Early exploration started in Figma; once the interaction model was proven, the Playground let me rebuild it as a working, reusable mapping pattern — so scaling it to a second product meant reusing the pattern, not redesigning it.
Complex data, explored as something real rather than something described.
Customer dashboards began as sketches and flows, then moved into the Playground as interactive, chart-driven prototypes. Reusable chart components and design tokens meant stakeholders were reacting to something they could actually use, not a static mock.
Realistic enough to test, safe enough to share.
A retail promotions concept explored almost entirely inside the Playground, with very little static Figma work. Reusable components and dynamic, fictional datasets — invented retailers, invented products, a fictional World Cup promotional campaign — meant the prototype felt real in usability testing without exposing a single piece of commercial data.
The same principle, someone else’s hands.
A designer I mentor, Dmytro Ivanchenko, took the same principle further — composing an AI agent from our design system’s existing components and documented usage rules, and separately building a plugin that converts a design between breakpoints automatically, cutting a task that took over two minutes by hand down to under thirty seconds. Composing from a proven system, it turns out, is a philosophy other people can pick up and run with — not just mine.
What I’ve learned
AI hasn’t replaced my judgement. It’s changed where I spend my time.
- Less time reproducing screens, more time understanding problems.
- Reuse compounds — every component built once pays for itself many times over.
- Realistic data matters as much as realistic interfaces.
- Prototypes that work invite sharper feedback than prototypes that merely look finished.
- The gap between “designed” and “buildable” narrows when design and engineering share the same components.
The design craft hasn’t gone anywhere — it’s moved, from producing individual screens to orchestrating the systems, evidence and reuse behind them.
Where this goes next.
Beyond generating interfaces, I’m increasingly interested in how AI can make validated product knowledge and research easier to discover, reuse and apply throughout product development.
One area I’ve been exploring with our research team is how this could become part of the Playground itself — not replacing research, but reducing the time spent searching for evidence, so teams can build on what they already know rather than starting from scratch.
See the same evidence-led discipline applied to a validated product direction.