AI design practice | Arity

AI design as a craft, not a shortcut.

I treat Cursor and Figma Make like any other foundation layer: onboarding, taxonomy, QA habits, then evidence. Nothing here stays in a lone designer's downloads folder - teammates adopted the guides, libraries, and skills because they shorten real cycles without hiding tradeoffs.

  • 2+ hrs Reclaimed weekly Automated Jira ticket hygiene + QA agent guarding acceptance criteria leaks.
  • 65% Product org adoption Twenty-eight of forty-three teammates onboarded via the Cursor guide.
  • 2 writers Enabled within an hour each Still active with brand agents sourced from canonical Confluence voice docs.
01

Patterns

Figma foundations translated for models

Started translating existing components into predictable token + variant language for Figma Make, then packaged the output as portable Figma + React bundles teammates could remix with Cursor. If the names are consistent, retrieval stays honest - hallucinations shrink when scaffolding is boring.

02

Docs

Cursor onboarding for design + adjacent roles

Co-authored onboarding that covers prompts, branching strategies, QA loops, failure modes worth rehearsing aloud, and when to hop back into Figma for fidelity. Adoption beat two-thirds across the forty-three-person orbit because it met people where sprint pressure already lived.

03

Voice infra

Brand alignment as reusable skills

Skills + Cursor rules distilled Arity verbal identity into procedural checks any workflow can call — copy experiments, conversational UI, onboarding strings. One source updated in Confluence propagated into every agent-ready surface.

04

Cross-skilling

Writers prototyping without waiting on design bandwidth

On request from leadership, ushered marketing writers through Cursor installs, seeded their brand-voice bot from existing documentation, and left them iterating autonomously. Goal isn't bypassing designers - it's freeing us for systems thinking while language experts own early drafts responsibly.

05

Rhythm

AI-assisted critique sprints

Repeated recipe: tight problem framing, enumerated prompts, review rubric borrowed from critique culture, disciplined handoffs back into the standard design/engineering checkpoints. Compressed days-long exploration into respectful half-day arcs without skipping ethics or accessibility checkpoints.

Sample artifact

Brand voice, made reusable

Production skills ramble intentionally - here's the gist in Markdown so teams know what rigor they're buying.

brand-voice.skill.md md 
---
name: brand-voice
description: Author copy that sounds like Andrew's Arity Perceptive Advisor shorthand.
---

# Voice principles
- Lead with the driver benefit - never bury the payoff behind telemetry jargon.
- Calm certainty: confident but reversible when new data appears.
- Name the source of any behavioral claim when space allows.

# Avoid
- Moralizing about driver choices.
- Hype adjectives ("revolutionary", "seamless AI magic").
- Passive voice that hides who owns the next step.

# Prefer
- Plain speech you'd use with a tired road-trip friend.
- Short clauses for glanceable mobile surfaces.
- Explicit "we / you" ownership on commitments.

# When generating UI strings
1. Start from the job someone is trying to finish, not the ticket title.
2. Pair every risk callout with the next action.
3. Self-check: would this sound fair if read aloud in a car?

Next step

Screens are the output. Want to see the thinking?

Happy to unpack the metrics, the process behind the tools, or how AI adoption stayed grounded while the org scaled.

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