Selected daily and weekly entries — what I built, what broke, and what it taught. The private half stays private.
I let an AI system build things for me most days. What lets me sleep on that isn't the model — models sound confident whether they're right or wrong. It's that I wrapped it in two controls any finance team would recognise on sight. One: the thing that builds never approves its own work — maker and checker are never the same actor. Two: assume most failures are quiet, so the system checks its own health and is built to make a sound when something breaks. On standing hold until you lift it.
A system that checks its own health: a daily heartbeat that confirms every job ran, and a weekly pass that catches when its own records drift apart — plus the day it found its own blind spot.
The hours didn't go on judgment — they went on work that shouldn't have been manual. The gap in audit isn't technology. It's execution.
Nine years ago I took a career break to study data science. What actually prepared me for AI in finance wasn't AI — it was data literacy.
An AI agent — not an RPA bot — processed a backlog of scanned contracts across eight languages overnight. The difference wasn't speed. It was judgment.