TL;DR

Two products I designed in unrelated domains, an sustainability data platform for financial analysts and a wound-assessment tool for elderly clinicians, taught me the same thing about AI: neither user can tolerate it being confidently wrong.

I designed against one rule: speed the expert up, without ever quietly taking the decision away from them.

Context

Both Zonder (clinical documentation platform) and Statsland (sustainability data platform) were shipped without AI. Using what I already knew about each domain, I went back and asked where AI could genuinely speed up the expert doing the work, without quietly taking the decision out of their hands.

Principles applied to trustworthy AI

Draft, don't decide

Show the seam

Fail loud, not silent

Entry point

The provider decides; nothing happens without this choice.

Draft, don't decide

AI works alone here, but commits nothing on its own.

Show the seam during success

Every AI-drafted field is visibly provisional until someone says otherwise.

Fail loud, not silent, upload

A gap AI admits to is safer than a guess it doesn't.

Show the seam, then draft, don't decide

Overriding a filter manually turns it from AI-set to human-confirmed.

Fail loud, not silent, discovery

One flagged number doesn't cast doubt on the rest of the page.

Show the seam, after the fact

Trust is a record, not just a moment. This outlives the review itself.

Same three principles. A different domain, a different failure mode, and one new proof point: trust has to survive a human changing its mind, not just a human agreeing with it.

Project 2
Zonder AI-assisted wound assessment

What I shipped
Digital assessment tool. 1.5hrs to ~14min, 92% preferred it, 59% more throughput.

How I reimagined it using AI 
Manual documentation mid-treatment. Now AI drafts from the visit, clinician confirms.

Entry point

Clinicians can switch to AI-assisted recording anytime

Show the seam during success

AI-suggested answers are confirmed or overridden by the clinician

Fail loud, not silent in visual interface

When AI isn't confident in a photo, it flags it for review instead of guessing

Draft, don't decide

Recording is in progress & nothing is saved yet

When AI can't confidently answer, it says so instead of guessing

Next steps (if I cont'd)

1st Open question: does drafting from ambient audio or an extracted report actually save time, or does reviewing and correcting AI's draft just replace typing time with editing time? That's a usability study, not an assumption, and it applies to both flows equally.

2nd open question: AI can be confidently wrong, not just honestly uncertain. Both concepts only design for the second failure mode. Where's the check for the first?

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