Design Thinking Process

“Time isn’t the main thing, it’s the only thing”
Couldn’t have said it better myself, Miles.

My design process has evolved to a simple, agile, system for plotting any product path forward, optimized for agility & speed. Being a founding designer on small teams has enabled me to develop skills for defining and prioritizing requirements as part of this process. Like any system, it’s a work in progress and gets tweaked with every iteration, but the basic tenets of the sprint cycles I’ve worked on have been:

🤔 Discuss the problem we’re trying to solve with leadership

📚 Read about the space, conduct research & competitive analysis, focused on market gap & target audiences

🗣 Engage with potential users via surveys & interviews, prioritizing surveys & screeners to optimize for expenses

⚖️ Discuss problem & research in context of the “dream product” vs. how to put the minimum in MVP

🧠 Come up with awesome ideas for later & put them in a prioritized backlog

✏️ Create & review detailed design MVP specs as structured data

🧑‍💻 Talk w/ Engineering: “How long will this take? What would make it even simpler?”

🗺 Create a living sitemap, where it’s easy to understand design & development progress visually

🧪️ Create quick low to mid-fi wireframes, prototype, & test basic functionality & copy effectiveness

🗣 Review w/ team, iron out issues, & remove unnecessary complexity for content clarity

👩‍🎨 Upgrade to hi-fi, limiting structured components (reusable design system = consistency & speed)

🎯 Deliver to engineers in granular detail to facilitate targeted feedback

🤝 Test Implementation & ensure team/stakeholder expectations are met

⏭ Move on to the next critical feature

This process repeats with the “minimum” changing depending on customer needs. For example, Weavechain’s process included end-to-end implementation of 62 user stories across 12 epics in 12 months.

Each subsequent product I’ve worked on has a few important documents that facilitate this process:

  • Data-driven User Research & Competitive Analysis Synthesis docs

  • Ranked User Stories, Feature Requirments, & User Testing spreadsheets

  • Journal of meeting agendas & discussion notes

  • Living sitemap & user flows

  • Figma project including assets, wireframes, & prototypes.

“None of us is as good as all of us”
Ain’t that the truth, Ray.

Perhaps the most important aspect of designing products from brainstorming to implementation can’t be captured in a document but is a critical part of my process:

I’m passionate about developing great relationships with engineers. I’ve found this essential to:

  • Facilitate realistic brainstorming

  • Understand the lowest-hanging fruit to influence prioritization

  • Provide designs that are optimized for efficient implementation

  • Ensure feedback is helpful and targeted for iteration

  • Make sure everyone is in the best mood possible & has what’s needed to get our best work done =)
    (Who wants to work on a team where people don’t think about how to facilitate colleagues’ workflows?)

✨ Leveraging Generative AI to Facilitate Efficient, Cohesive Design

I’m always looking for ways to optimize efficiency and have been digging for best ways to incorporate generative AI into my workflows. Some of the places where I have found it effective have been:

  • Generating lists & summaries to spark competitive diligence or target user roles & habits

  • Discovering & summarizing sources for user & product market research

  • Learning new concepts to understand customer-specific needs in jargon-heavy industries (finance, AI/ML, etc)

  • Transcribing & Synthesizing anonymized qualitative user interview data

  • Drafting, refining, & editing content for better, more realistic & helpful copy

  • Supporting branding brainstorming, incl. color palettes, fonts, logo inspiration, etc.

  • Generating imagery for hi-fi wireframes

I search for, draft, refine, and save especially effective prompts and look for ways to improve my prompt engineering for quicker, better results. I stay vigilant about best practices for ethical AI use, ensuring leadership’s buy-in before using any tools as well as keeping sensitive company information, personal data privacy, & copyright considerations top of mind.