Leveraging AI/ML/Mobile and Automation Features
What this domain tests
This domain tests the practical use of automation and assisted entry in the invoice-to-payment flow: IDR, invoice imaging, supplier portal, mobile expense capture and approvals, scheduled processes, and AI-assisted review.
Core rules
- Intelligent Document Recognition uses OCR plus machine learning to extract supplier, invoice number, date, amount, tax, and line data from scanned or PDF invoices. Confidence scores determine whether a document posts automatically or goes to review.
- Invoice imaging captures documents and attaches them to the invoice; IDR is the recognition layer, not simply storage.
- The supplier portal lets suppliers submit invoices and check status, reducing manual entry. It does not replace validation, matching, or approval.
- Mobile Expenses supports receipt capture, expense entry, and approvals. Mobile approval of an invoice or expense still applies the same approval rules and holds.
- Scheduled processes and payment process request templates automate repetitive runs, such as recurring invoice import or a weekly payment run.
- AI and automation reduce keying and routing effort, but they do not override tolerances, holds, tax rules, or segregation of duties.
Worked example
A PDF invoice arrives by email. IDR extracts the header and lines with high confidence for supplier and amount but low confidence for one line. The invoice is created and routed to a reviewer for the uncertain line. After correction, the invoice proceeds to validation and matching. The correction also improves future extraction; it does not auto-approve the invoice.
Traps
- Treating OCR and IDR as identical.
- Assuming automation skips validation or approval.
- Using mobile approval without checking the underlying hold.
- Expecting IDR to fix poor scan quality or missing supplier data.