Problem - Our design team updated a shared doc every week — find the right spot, copy a table, fill it in, don't overwrite someone else's section. Slow, error-prone, and entirely manual. I wanted to know if conversational AI could replace the whole workflow.

What I tried and what I learned - Plain-text input was the obvious starting point. It failed fast — the AI mixed up data across projects, assigned links to wrong entries, merged descriptions that didn't belong together. I switched to an emoji-tagged template where each emoji acts as a parsing anchor. Still just a chat message, but now the AI has reliable structure to extract against.

The shared table broke next. One row per person couldn't handle different project counts, and concurrent edits wiped each other out. I restructured it so each person gets their own named block — same doc, isolated data, names visible in the sidebar.

The most deliberate decision was what I cut. I'd built an image upload flow — drop screenshots in chat, system embeds them in the right cell. It worked for simple cases, fell apart with multiple images across multiple projects. Matching was unreliable, confirmation flow was tedious. I removed it. Text goes through AI; screenshots go directly into the doc. The real design call was knowing where to draw that line.

How it works - Trigger the tool, fill in a template, confirm a summary — done. Concurrent submissions, formatting, and archival all happen behind the scenes.

Shipping it - I packaged it as a shareable AI skill — one link to install, I own the code, teammates just use it. Push an update and everyone gets it on next use. No re-installs, no version mismatch. Prototype to team tool in under a minute.

Takeaway - AI-native design isn't about automating everything — it's about knowing what AI handles well, what it doesn't, and making the seams invisible.