Each one started as a personal annoyance and ended as a repeatable system. Some live, some portfolio.
Automations fail silently — you only notice when something downstream breaks days later. So I built two checks on top of the work: a daily heartbeat that confirms every job actually ran, and a weekly pass that cross-checks whether my own notes, plans and status still agree. Blind spots surface as alerts, not nasty surprises.
Read the build note →Letting an AI build and change things unsupervised is how quiet mistakes reach production. So I borrowed the control every finance team already trusts — four-eyes review — and wired it into the machine: every piece of automated work runs plan → build → an independent fresh-eyes audit, and nothing ships without a human approving it.
Ask me about it →A newly merged, private-equity-backed group with no shared system and board reporting stitched together by hand each month. Over a year I designed the data model, automated the reporting, and stood up the first standard workflows — turning thirteen-step manual processes into one. The lesson: AI didn't win on model quality, but on ownership, process and clean data.
Ask me about it →Map the manual steps and lost context before writing a line of code.
One automation that removes one step, wired into existing tools, documented to hand over.
Schedules and alerts that only fire on failure. Silence means healthy.