Automation Delivery Checklist

A production automation project should read like an operations change programme, not a Zapier demo. The work starts by proving which workflow is worth automating, then turns that workflow into a controlled system with ownership, exceptions, metrics, and a support model.

This breakdown uses a common example: invoice intake, approval routing, and ERP posting for a mid-market operations team.

A finance operations team receives invoices through email, supplier portals, and shared folders. Staff manually rename files, enter invoice details into a spreadsheet, check purchase orders, chase approvals, and then post entries into the accounting system.

The work is repetitive, but the risk is real. A missed approval, duplicate invoice, or bad vendor match can create audit issues. The automation project needs to reduce manual entry without removing the controls finance depends on.

• Identify the exact workflow boundary: invoice received to approved accounting entry.
• Capture baseline volume, average handling time, rework rate, approval delays, and exception categories.
• Map every system involved: inbox, document storage, ERP, purchase order data, approval tool, dashboard, and audit archive.
• Confirm who owns the process, who approves changes, and who handles exceptions after launch.

Example baseline: 1,400 invoices per month, 7 minutes average handling time, 14% requiring rework, 3.8 days average approval cycle, and three finance staff touching the process weekly.

• Intake layer: monitored mailbox, portal export, or folder watcher.
• Extraction layer: OCR/document AI reads supplier, date, amount, tax, line items, PO number, and payment terms.
• Validation layer: match supplier master, PO, duplicate invoice rules, tax rules, and required fields.
• Orchestration layer: route approvals based on amount, department, and exception type.
• Posting layer: prepare ERP draft entry or API posting with evidence attached.
• Monitoring layer: show pending approvals, exception backlog, cycle time, and automation success rate.

The important design decision is where the human stays in the loop. Low-risk invoices can move to approval automatically. Exceptions should be queued with clear reasons instead of silently failing.

• Build a test set from real historical documents, including messy scans and edge cases.
• Configure validation rules before connecting the workflow to production posting.
• Create exception categories such as unmatched supplier, PO mismatch, duplicate invoice, missing approval, and tax variance.
• Add audit logging for every system decision and human override.
• Test with shadow mode before the system becomes the primary workflow.

Shadow mode example: the automation prepares entries for two weeks while finance continues the old process. Differences are reviewed daily until confidence is high enough to switch.

• Start with one invoice category, region, or vendor group.
• Train approvers on the new queue and escalation path.
• Run daily hypercare for the first two weeks.
• Track adoption by team, approval cycle time, exception rate, and manual override rate.
• Keep a backlog for rule improvements and new document types.

A good launch does not chase 100% automation on day one. It proves that the most common, lowest-risk cases can move faster while exceptions become easier to see.

• Manual entry time drops by 40-60% for in-scope documents.
• Approval delays become visible in one dashboard.
• Exceptions are categorized instead of buried in email.
• Audit evidence is attached to each entry automatically.
• Finance retains approval control while reducing re-entry.

Use this as a gating document. If the baseline, exception model, audit trail, and support owner are not defined, the automation is not ready for production.