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How I Think,
Research & Build.

Three lenses I use across projects in the age of AI.

🧭Strategy Design 🔍Design Research Process AI & Vibe Coding
answer
💡 Why You Should Hire Me

Jenny Wen, former Director of Design at Figma, left her role to return to IC work at Anthropic. Andrej Karpathy, OpenAI co-founder and the person who coined "agentic engineering," named what's actually happening to our field.

Two things matter more than ever as a designer: engineering craft, and the judgment to direct it.

Strategy is the most
upstream form of design.

One of the biggest mindset shifts I got from Parsons was learning when to step back from the problem entirely.

Most people enter a business challenge at the subject level — optimizing a product, fixing a process, refining a feature. However, the first question I ask is whether we are looking at a symptom or at the system producing it. This is where strategic design separates from UX design; it is a judgment call driven by systemic thinking.

Much of this thinking was shaped by my professor Raz Godelnik, whose work on strategic design continues to influence how I approach complex problems.

Business Strategy

Two frameworks I use to read the strategic environment before any design work begins.

BCG Strategy Palette

Before I define a design direction, I need to know which strategic environment the business actually operates in — predictable, adaptive, harsh, or visionary — because each demands fundamentally different design logic.

BerylElitesX

The Palette revealed a mismatch: Adaptive environment, Classical design logic. That reframe reshaped the strategic brief.

Three Horizons

My practice is to establish H1 and H3 before engaging H2. Without those anchors, the middle horizon drifts into optimism or paralysis. Roger Martin's question sharpens the work further: "What would have to be true for this transition to be possible?"

Google Maps Sustainability

Work sits in H2+. H1 = existing eco-certification infrastructure. H3 = a world where sustainable choices are effortless and invisible. The design question only becomes precise once both ends are established.

Three Horizons Framework — H1, H2, H3

Referenced · Roger Martin's Strategic Choice Structuring Process

Roger Martin — Strategic Choice Structuring Process

Product Strategy

Where strategic thinking connects directly to what gets built, how it's positioned, and how it enters the market.

Roadmap Prioritization

An impact vs. effort matrix turns team debate into a decision by making judgment criteria explicit — what to build now, what to watch, and what to drop.

Bayer

After framing four opportunity directions from research, a core team session aligned on prioritization and stakeholder buy-in in one room.

Market & Competitive Analysis

I combine Porter's Five Forces with the BCG Strategy Palette to analyze industry power dynamics and strategic environment simultaneously.

Parkonomy

The analysis revealed the competitive moat wasn't in the parking transaction — it was in the data layer. That reframing changed the entire product direction.

Go-to-Market Thinking

GTM is a product decision. Entry point determines which features matter, which partnerships are essential, and which users get prioritized first.

Parkonomy

B2B entry targeting property managers over individual drivers — validated with a letter of intent from one of China's largest real estate firms.

Research in the Age of AI:
When to Stop and Ship

Most designers today use AI in some form. That part is no longer interesting.

The harder question is how to meaningfully integrate AI into a design process without losing the judgment that makes research worth doing. The whole industry is still figuring that out in this extremely fast-changing environment.

However, I am still able to contribute some ideas based on my experience and continuous thinking.

1. Match the process to the project

In the podcast, Jenny mentioned that the old linear design gospel — research, diverge, converge, prototype, repeat — can no longer hold its shape when engineering moves this fast. Something has to give.

But what should give, and by how much. My answer is that it depends on how much ambiguity the project carries before the first prototype exists. I assess this across three dimensions. User diversity, Feature complexity, Stakes.

The Bayer project sat high on all three. That's why we spent four weeks on research and opportunity framing before anything got built.

High-nuance
Low-nuance

High user diversity

Homogeneous user group

Complex, interdependent features

Contained feature scope

High cost of wrong direction

Low iteration cost

For high-nuance projects, I follow the original design thinking structure. Primary and secondary research run before problem definition is locked. Prototyping only begins once the problem space is understood, but at that stage AI accelerates everything.

For low-nuance projects, the process compresses. Primary research establishes directional confidence, then the work moves straight into an AI-assisted design innovation sprint. Rapid problem definition, then immediately into AI prototyping.

High-nuance vs Low-nuance research process diagrams showing iteration loops

The signal to stop is the same in both cases. When a working prototype will tell you more than another round of research, ship the prototype.

2. The second layer: dark matter

This is where most research stops too early. Dan Hill's concept of dark matter describes the invisible forces surrounding any design intervention — organisational culture, power structures, policy constraints, institutional inertia. The product is visible matter. Everything determining whether it can survive and scale is dark matter.

User research tells you if people want the thing. Dark matter research tells you if the system will allow it. A solution that users love but contradicts an organisation's incentive structure will not land. Identifying that early is not pessimism. It is the difference between research that informs a decision and research that only describes a problem.

The methods toolkit below remains the same. What changes is why and when each method gets used.

3. Methods Toolkit

I work across qualitative and quantitative methods. Selection from the methods toolkit depends on the project.

🎙️

In-depth Interviews

Qualitative
👁️

Contextual Inquiry

Qualitative
📊

Surveys & Screeners

Quantitative
🖥️

Usability Testing

Mixed
🗂️

Card Sorting

Qualitative
🗺️

Journey Mapping

Mixed
🧩

Affinity Mapping

Qualitative
🔬

Heuristic Evaluation

Quantitative
📈

Analytics & Behavioural Data

Quantitative

4. My Research Process

01

Define the Research Question

What do we actually need to learn? What decisions will this research inform?

02

Select Methods & Recruit

Method selection follows the question.

03

Field Work & Data Collection

Interviews, observation, surveys — capturing raw data without interpretation.

04

Synthesis & Sense-Making

Affinity mapping, thematic coding, insight laddering. The goal: compress raw observations into patterns, and patterns into insights.

05

Translate to Decisions

Research ends when a decision gets made. Make sure those insights have a receiver.

The workflow
is not linear.

Figma to code. Code to Figma. Code to code. The entry point shifts with every project. I move between tools fluidly.

The Tool Constellation

Any node can be the entry point. Arrows can run in either direction.

AI tool constellation diagram showing Claude Code at centre with bidirectional connections to Figma Design, HTML, Adobe, VS Code, output types and inspiration sources

© Sienna Chen. Original diagram — do not reproduce without permission.

What makes the workflow work

🏗

Information Architecture

After research, before code. Manual or AI-assisted depends on project scope. Here's how you decide →

✍️

AI-Readable Language

Design literacy = precise prompts. Can't describe the effect? Prototype it first, then show Claude what to refine.

🎨

Design System Borrowing

Pull real CSS from DevTools or Stitch, pass to Claude. Grounded output — not generic AI defaults.

The stack I work in

🎨
Interface HTML · CSS · Tailwind · React

Where design becomes code. The layer closest to Figma.

⚙️
Logic JavaScript · TypeScript · APIs

Interactions and data flow. Read it well enough to direct AI — don't need to master it.

🗄️
Data Supabase · PostgreSQL · REST

Schema, auth, storage. Supabase makes it a prompt away.

🚀
Deploy Vercel · GitHub

Push → live URL in 30 seconds. One-time setup, invisible after.

The AI-First Tool Stack

🎨 Figma design intent
🤖 Claude Code writes the code
💻 VS Code edit + navigate
🗄️ Supabase data + backend
🚀 Vercel ship live

4.1 · What Kind of Designer I Am

I'm a Full-Stack
Designeer.

In the podcast, Jenny Wen described three kinds of designers the industry tends to value in the age of AI — Strong Generalist, Deep Specialist, and Crack New Grad. I'm a hybrid of the first and the third. I call myself a full-stack designer.

That's me

Full-Stack Designer

♟️ Strategy 🔍 Research 🎨 Design 💻 Code

Operates across the full problem stack — from strategic framing to shipped product. I'm also very resilient and adapt to project phases.

At Beryl Consulting and Parkonomy, I operated in this full-stack capacity from day one. A personal project, a sustainability service redesign for Google Maps, is currently in progress. At Bayer, I have deepened my user research and product strategy skills through close team collaboration.

4.2 · My Commitment

23

A personal note

I'm 23, two years in, and already operating across strategy, research, design, and code in real client engagements. I have deliberately chosen roles where the full stack was required.

In 2–4 years, I will deepen into a specialist in your company.

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