Adapt One Read-Only Automation to Your Own Org's Export

You've learned four distinct read-only workflows in this track: expense categorization, reconciliation flagging, KPI dashboards, and report drafting. This capstone asks you to do the thing that actually changes your job — pick exactly one, and adapt it to your own org's real accounting export, not a hypothetical or sample dataset.

Step 1: Choose your one automation, deliberately. Pick whichever of the four will save you the most real time or catch the most real risk, given your actual role. If you spend hours each month manually sorting expenses, choose categorization. If your reconciliation process is currently a source of quiet anxiety about what might be slipping through, choose reconciliation flagging. If leadership keeps asking for numbers you scramble to assemble, choose the KPI dashboard. If report-writing eats your last week of every month, choose reporting automation. Don't try to build all four — one, done properly and actually put into use, is worth more than four built halfway.

Step 2: Pull your own real export. This is the step that makes the capstone concrete instead of theoretical. Get the actual export format your org's accounting system produces — the real column names, the real date formats, the real vendor-name quirks, the real scale of data you'll actually be working with month to month. If your chosen automation is categorization, that's your real expense or transaction export. If reconciliation, your real bank feed and ledger export. If dashboards, your real cash, AR, and budget exports. If reporting, your real verified KPI and categorization output from a recent cycle.

Step 3: Rebuild the workflow spec against your real data, not the course's examples. Revisit the course covering your chosen automation and redo the concrete design work — your actual category taxonomy and vendor patterns (if categorization), your actual reconciliation flag report structure (if reconciliation), your actual KPI set and data dictionary (if dashboards), your actual five-section report structure (if reporting) — using your own org's real terminology, real thresholds, and real named reviewer, not the illustrative examples from the lesson.

Step 4: Run it once, completely, on real (or very recent real) data. Not a mock dataset — an actual month, or the most recent real cycle available to you. Follow the full workflow: read the export, apply AI where the course taught you to, produce the flagged/categorized/summarized output, and confirm it stays entirely on the read-only side of the line — no auto-posting, no auto-writing, no auto-initiating anything.

What "adapted" means, concretely, as your deliverable for this lesson. By the end of this lesson you should have: a written, org-specific version of your chosen workflow's spec (ruleset, flag report structure, KPI data dictionary, or report structure), and one completed real run of it against your own actual data, with a named human reviewer (likely you, for now) having gone through the output.

▶️ Try this

Choose your one automation now, and write down, in one sentence, which of the four it is and why you picked it over the other three. Then pull your real export and complete steps 3 and 4 above before moving to the next lesson — the validation checklist there assumes you have a real, completed run to check against, not a plan to build one eventually.