1 min lesson
Design AI/agent automation for finance operations responsibly
Use "Design AI/agent automation for finance operations responsibly" to tell the cases apart, then choose a response for each one.
Step 1 of 2
Start by picking work that is high-volume, judgment-light and currently eats manual hours. Those are where an agent pays off and where a mistake is cheap to catch.
Match usage→invoice→payment→GL across systems.
Agent proposes matches and flags the residual; a human clears exceptions.
Measure: % of lines auto-matched, exceptions left for review.
Surface a usage spike, a margin outlier, a duplicate invoice.
Output is an alert with evidence, not an automatic adjustment.
Measure: true-positive rate, time-to-detect.
Draft outreach, sequence reminders, propose write-off candidates.
Sending is fine; writing off requires approval.
Measure: DSO movement, recovered AR.
Use Cursor to scaffold an ETL job, a webhook handler, a migration.
Output is reviewed code in a PR, never a live change.
Measure: build time, defects caught in review.