There's a common assumption in intelligent document processing: the closer you get to 100% straight-through processing, the better. No human touches, no delays, no cost. It sounds compelling - but for organisations processing invoices, insurance documents or customs declarations, chasing that number can lead them in entirely the wrong direction.
The reason is simple: IDP doesn't exist in isolation. Understanding and extracting data from documents sits in the context of wider business process such as P2P. A ‘processed’ invoice flows downstream into an ERP system, triggers payment runs, updates supplier accounts, and feeds financial reporting. A freight document feeds into logistics planning, customs clearance, and inventory management. When something is processed incorrectly at the document stage, the error doesn't disappear - it travels downstream. A strong upstream system, for example effective purchase ordering, reduces many of these problems and improves accuracy and automation. However, there are always edge cases and exceptions and in some sectors there may be suppliers that are unable to comply with the process – consider self-employed sub-contractors.
And by the time the ‘error’ surfaces in the ERP, the cost of fixing it has multiplied. A misread figure or missed discrepancy that could have been caught in seconds during document review might require a payment reversal, a supplier credit note, corrected journal entries, and a trail of correspondence, including human sign-off, to unpick. In high-volume environments, these aren't edge cases - they're a predictable consequence of optimising for throughput over accuracy.
The real goal isn't automation. It's accurate, confident decisions at the point of processing - because that's where intervention is cheapest.
That distinction changes how you design the system. When automation is the goal, human review feels like a failure state - something to be minimised and eventually eliminated. When accuracy is the goal, human-in-the-loop becomes a deliberate feature. Reviewers aren't plugging gaps; they're providing validation on the cases that matter, catching what the model flags as uncertain, and generating the feedback that makes the system smarter over time.
This is why effective IDP embraces the human-in-the-loop. Every exception a reviewer resolves is a data point. Every correction feeds back into the model. The humans aren't slowing the process down - they're teaching it and protecting the integrity of everything that happens downstream.
There may be an opportunity for AI-agents to help us by autonomously identifying low-confidence cases and working out a means to correct them. This is a rapidly evolving area of AI development but we should consider that even autonomous correction requires resources and therefore introduces costs. Furthermore, we need to understand HOW the autonomous agents concluded on a course of action and exactly what they did. Could this intervention cloud the transparency and accountability of the system? Could the quest for 100% lead us down an ever more complex and costly dead-end? Ultimately, prevention is structurally cheaper than correction - even when correction becomes smarter. And there's a deeper point: agentic systems learn from data too. The question is whether that data has been validated. Human-in-the-loop at the IDP stage doesn't just prevent downstream errors - it generates the clean, reviewed signal that any future agentic layer depends on.
Organisations that pursue maximum automation could find themselves with high throughput and low trust - processing quickly, but absorbing errors that surface later in the ERP, in supplier relationships, or at month-end close. The hidden cost of removing humans from the loop is frequently greater than the visible cost of keeping them in it.
The better question isn't "how do we automate this?" - it's "how do we make every decision one we can stand behind, all the way through the process?" Sometimes that's the model running at high confidence with no review needed. Sometimes it's a human casting a trained eye over something the system has flagged. A well-designed IDP solution knows the difference.
Automation is a tool. Accuracy - and the business process integrity that depends on it - is the goal.
