Which Documents Actually Need Your Attention?

An invoice with a missing purchase order number. An application with incomplete information. A document that looks routine but contains a value outside your team’s approval threshold. Each needs a different response. Yet when every document enters the same review queue, employees spend time checking routine information alongside exceptions that deserve closer attention. A useful next step in document automation is deciding where human review adds the most value and defining the conditions that bring those documents to your team.

Start with the reason for review

“Someone should check this” is a starting point. To build a repeatable process, make the reason specific. Is a required field missing? Does an amount exceed an internal threshold? Does the extracted information need an adjustment before another system can use it? These questions help distinguish recurring requirements from situations that need individual judgment. They also give your team a clearer basis for configuring the workflow. Consider invoice processing. A missing reference and an unusually large total may both require attention, but for different reasons. Identifying those reasons makes it easier to decide what should happen next.

Put those conditions into the workflow

Base64’s Teach AI lets teams configure rules that define when an action should occur. Actions can include updating a field or setting a document’s status to Auto-approved, Needs review, or Rejected. For example, a team can configure invoices above a defined amount to receive Needs review status. A separate rule could address a recurring supplier-specific extraction requirement. Each rule serves a particular purpose within the process. This gives teams a practical way to apply established requirements while keeping reviewers involved where their judgment matters.

Test the boundaries

A review rule should be evaluated against more than the document that inspired it. Try examples that meet its conditions, examples that should pass without intervention, and cases close to a threshold. Check whether the outcome matches your team’s expectations. Teach AI provides a preview on the current document, showing whether conditions match and what changes would result. Teams can refine the rule before saving it for newly processed documents. Broader testing then helps assess whether the rule is useful across the variety of documents your team receives.

Measure the work that remains

When evaluating a workflow, look at what reviewers still need to do:

• Which issues appear most often?

• Which checks follow a clear, repeatable requirement?

• Which exceptions take the longest to resolve?

• Are documents reaching the next system with the information it needs?

The answers can guide your next improvement, whether that means refining extraction, adjusting a rule, or clarifying a review step. Start with one reason documents keep coming back for attention. Define it, test it, and build from there. Schedule a conversation with our team to explore how Base64 can support your document review process.