We’ve been running a process mining tool for about nine months now and have solid visibility into our order-to-cash and procure-to-pay flows. The dashboards show clear bottlenecks – approval loops that take three times longer than expected, manual re-keying between our ERP and legacy invoicing system, and exception paths that eat up half our team’s time. The problem is we can’t seem to turn these insights into actual automation. We have some older RPA bots handling data entry tasks, but they’re brittle and break whenever anything changes.
What we’re struggling with is the middle layer. Process mining tells us what’s broken, RPA can execute predefined steps, but how do you connect the two in a way that actually handles real-world variability? Our exceptions are messy – invoices from new vendors, orders with partial shipments, contract terms that don’t fit standard rules. The bots just fail or escalate everything to humans, which defeats the purpose.
Has anyone successfully integrated process intelligence with automation in a way that can interpret context and decide what to do with edge cases? Are we supposed to be looking at decision engines, or is this where AI orchestration layers come in?
One thing that’s helped us is focusing on a narrow set of exception types first. We identified the top five exception patterns that accounted for about 60% of our manual interventions – things like missing PO numbers, vendor mismatches, amount discrepancies above certain thresholds. We built specific logic for those using a rules engine, with AI only handling the truly novel cases. It’s not perfect but it got us from 30% automation to about 70% within six months, and the bots are way more stable because the logic is explicit.
Don’t underestimate the governance piece. When you start using AI to make routing and exception-handling decisions, you need full auditability and explainability, especially in finance and procurement. We log every decision the AI makes – what data it saw, what confidence score it assigned, what action it recommended – and that log feeds back into our process mining tool so we can see patterns in how exceptions are being handled over time. That closed-loop visibility has been critical for trust and continuous improvement.
You need a hybrid architecture. Use AI for the interpretation piece – understanding messy invoices, classifying intent, scoring confidence on whether something fits known patterns. Then use RPA for the deterministic execution once a decision is made. The pattern we follow is AI handles input processing and edge case analysis, RPA handles system updates and data entry. For exceptions, we set confidence thresholds. If AI confidence is above 85%, the bot executes automatically. Below that, it routes to human review with AI’s recommendation attached. This keeps your bots from breaking on every new scenario while still automating the bulk of standard cases.
Are you treating your RPA bots as part of a broader workflow or as standalone scripts? We had the same brittleness problem until we redesigned our bots to be modular components that get called by a central orchestration engine. The bots just do one thing – validate a field, update a record, trigger an API call – and the orchestrator decides the sequence and handles retries and error paths. That separation made a huge difference in maintainability.
How are you handling the data integration between your process mining platform and the RPA orchestrator? That’s been our biggest pain point. The event logs and process state data need to flow in near real-time to make dynamic routing decisions, but our infrastructure wasn’t set up for that kind of streaming integration.