An AI workflow can draft a buyer update, classify an incoming document or prepare a first pass at a diligence answer. The difficult design question is not simply whether a person should review the result. It is what the reviewer needs to decide, what evidence they need in front of them and what happens next.
Put a checkpoint where a consequential uncertainty becomes actionable. If a system cannot reliably match an email to a mandate, ask a person to choose the mandate before recording the activity. If an extraction is supported by a clear source, let the reviewer verify that claim directly. An approval step that hides the underlying uncertainty creates work without meaningful oversight.
01
How do you separate routine handling from judgement?
Begin by writing down the workflow as it happens now. Mark the steps that follow a stable rule, the steps where people compare context, and the decisions that belong to a partner or client. An agent may prepare material and route it; the team should decide which actions can proceed automatically and which require an explicit person to take responsibility.
For example, a system can collect a draft document, identify its likely mandate and prepare a record update. A confident match might be suggested, while a similar client name or unclear matter reference sends the item to review. The person sees the alternatives and the reason for the suggestion. The system records the choice and continues from there.
02
Why show the evidence beside the proposed action?
A reviewer should not have to reconstruct how the system reached its proposal. Show the source message or document, the relevant passage, the proposed change and the current record together. Distinguish extracted facts from inferences, and label any value that could not be checked against a source.
The NIST AI Risk Management Framework calls for human-oversight processes to be defined, assessed and documented. In a practical workflow, that means specifying the reviewer, the decision they make, the information they receive and how their choice is recorded. A green check or a model-generated confidence number cannot answer those questions by itself.
03
How do you make exceptions smaller than the whole task?
When one field is uncertain, route that field for review instead of stopping the entire workflow. A person might confirm the correct mandate while the system continues to capture the sender, date and attachment reference. If the whole item must wait, explain why and show what is needed to unblock it.
Keep the options clear: confirm, correct, defer or reject. A correction should update the record and, where useful, capture the reason. Deferral should leave the item visible to its owner. Rejection should not quietly become an approval on the next automated pass.
04
How do you test the checkpoint with difficult cases?
A clean demonstration rarely tests the hardest part. Use examples with similar client names, forwarded chains, duplicate attachments, changed figures and missing context. Ask reviewers to complete the task without coaching. Notice where they need to open another tool, which evidence they cannot find and which warnings they ignore because they appear too often.
Measure the time and correction rate at the decision point, along with the number of unresolved items and the consequences of a wrong choice. The aim is not to maximise automation. It is to move repetitive preparation to the system while leaving the consequential judgement with the right person, at the moment they can act on it.
Written by Ilyas Berel and Shihab Berel, Verdalyze. Published 10 October 2026.
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