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I’ve been mapping out our order-to-cash cycle and hit a wall trying to figure out where to apply robotic process automation for the best return. The sheer volume of manual invoice matching and data entry seems like the obvious target, but I’m worried that’s just the most visible pain point and not necessarily the most impactful one for the overall workflow.

Yep we tried the obvious win with RPA for invoice matching to POs and receipts. We ran a week-long pilot using a rule-based engine plus OCR for PDFs. We cut manual entry time in the invoicing team by about 40 hours a week and errors dropped, but cash application lag stayed because of PO changes and data hygiene issues. The ROI looked good on paper, but ongoing maintenance and master data churn ate into it.

Part of me wonders if the real brake on cash flow is the collections handoff and dispute rate, not the data entry. Even with clean invoices, if the customer disputes or the invoice lands in the wrong queue, payment slows. In our numbers AR aging only moved a little after automation, which made me pause and rethink what to automate next.

We did a quick one-week pilot on extracting data from supplier invoices and matching to PO lines. It shaved a few minutes per line, but ERP integration and master data sync kept breaking. After a few days we pulled the plug and just kept tracking exceptions in a separate dashboard. It taught me that speed without clean data is a trap.

Sometimes I drift to the order creation side, wondering if the choke point is upstream. We looked at how orders get approved before there is an invoice, and that stuff is messy too. But I circled back to invoicing because it felt like the low-hanging fruit, even if I’m not sure it’s the real root.

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