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AI is everywhere. It’s great at making new things. But can it make existing ones better?
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This case study follows how I explored AI as a tool to audit, redesign, and deliver a more consistent icon system (while the technology itself was rapidly evolving!)
Result: The icon system was successfully redesigned with 70% less effort than a fully manual approach.
Our icon system wasn’t dramatically broken. Instead, years of everyday product work had introduced small inconsistencies: mismatched weights, inconsistent sizes, conflicting metaphors, and icons that occasionally fought each other when used side by side. These inconsistencies increased maintenance, slowed design work, and gradually reduced trust in the library.
The Iconography Refresh had been sitting in the backlog for more than five years: updating 400+ icons manually was too expensive. As AI tools became more capable, I asked:
Could AI change the economics of this project?
With help from ChatGPT and Claude, I compared visual assets, inspected SVG structures, clustered similar icons, and identified patterns across the library, flagging inconsistencies and surfacing redundancies. The audit revealed a strong foundation: a mostly flat, minimal visual language. It also uncovered opportunities to simplify outdated categories and establish filled and outlined icons as deliberate system choices.
Time savings. A manual audit of 400+ icons was estimated at ~10 days. With AI support, the audit took ~2 days.
Icon set audit: strengths.
Icon set audit: pain points.
The first round was intentionally broad: I tested multiple tools and models and pushed them across many icon types — from simple UI shapes to detailed metaphor-heavy illustrations.
Goal: Find out whether AI could generate usable SVG icons. Takeaway: AI could handle the easy wins (basic shapes), but it wasn’t a design partner yet. Tools: ChatGPT, Copilot, EVA (Claude Sonnet), etc.
Side-by-side comparison of AI outputs against the current icon set.