Introduction
Wipro HOLMES is Wipro's AI and automation platform, used for cognitive process automation, virtual agents and predictive systems, and as a foundation for other Wipro offerings. One common use case is invoice processing in accounts payable (AP): reading supplier invoices from email or PDFs, extracting fields, matching them to purchase orders and goods receipts, and posting them to the ERP. Suppose a client's AP team, processing about 40,000 invoices a month, says the automation "isn't working": many invoices still end up in exception queues, the team spends hours correcting extracted data, and month-end close is stressful. The question asks how to improve this HOLMES-powered automation for the AP team. I'll clarify, segment users, map the journey, analyse pain points, generate solutions, prioritise and define metrics.
Process automation products should be judged on straight-through processing (how much work flows without a human touch) and on how easy the remaining exceptions are to handle. Improving only one usually disappoints users.
Step 1
Clarifying Questions (5 mins)
- Current performance? Assume straight-through processing (STP) of 45%, with 55% of invoices needing manual work; field extraction accuracy of 88%.
- Invoice mix? About 60% PO-based, 40% non-PO (utilities, services); 30% from the top 50 suppliers, the rest from a long tail of 4,000 suppliers; multiple languages and formats.
- Systems? SAP as the ERP; invoices arrive by email and supplier portal.
- Goal? Raise STP and reduce effort per exception, while keeping payment accuracy and compliance.
- Team? 25 AP processors in a shared services centre in Bengaluru serving operations in India, Europe and the US.
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