AI does the work. You keep the say.

What human in the loop automation means in practice

Most business processes are a mix of easy cases and hard ones. A standard invoice from a regular supplier, a routine change request, a familiar order. Then the odd ones: a new customer, an amount that looks wrong, a message that could mean two things. Treating every case the same, either all by hand or all by machine, wastes time or invites mistakes.

Human in the loop automation splits the work. The automation reads the input, does the preparation and decides which path a case should take. Clear, routine cases go straight through. Anything that fails a check or falls outside agreed limits goes to a person, with the work already prepared and the reason for review spelled out.

The person then confirms, corrects or rejects. Their decision completes the case, and over time it also shows where the automation can be trusted with more and where it still needs help.

• The automation prepares every case: reads, extracts, matches and checks.
• Clear cases that pass every check are completed automatically.
• Unclear or risky cases go to a review queue with the reason attached.
• A person confirms or corrects, and the case continues.
• Corrections are recorded so the flow can be improved.

Where to put a human review step

A review step costs time, so it belongs where it prevents real damage. The question to ask of each action is simple: if this went wrong, how bad would it be, and how quickly would we notice? Actions that are costly, visible to customers or hard to undo deserve a check. Actions that are cheap and easy to correct usually do not.

Uncertainty is the other trigger. A good automation knows when it is unsure: a field it could not read, a customer it could not match, two possible interpretations of a request. Those cases should go to a person instead of being guessed. It is far better for the system to say it does not know than to be confidently wrong.

Finally, some decisions should stay human by design, whatever the automation’s confidence. Approving a large payment, refusing a customer or making an exception to policy are judgement calls, and people expect a person to make them.

• Actions above an agreed value or volume.
• Anything sent to customers for the first time or in a new situation.
• Cases where a required field is missing or could not be matched.
• Changes that are hard to undo, such as payments or deletions.
• Decisions that involve policy exceptions or judgement.

Designing review steps that do not slow the team down

A badly designed review step turns automation back into manual work. If reviewers have to open three systems to understand a case, they will either spend too long on it or start approving without looking. Neither is what you want.

Good review steps show everything in one place: the original input, what the automation extracted or proposes, and exactly why it stopped. The uncertain field is highlighted. Approving takes one click; correcting takes a few. The reviewer focuses on the one thing that needs a human, not on the whole case.

Think about who reviews and how fast. A queue that nobody owns becomes a backlog. Assign clear ownership, decide how quickly cases need an answer, and make sure there is cover when that person is away.

Moving from full review to more automation over time

Most automations should start with more review than they will eventually need. In the first weeks, a person checks most or all cases while the automation proposes what to do. Comparing the two shows where it is reliable and where rules or data are missing.

As the results stay consistent, you let the simplest cases through on their own and keep reviewing the rest. Then you widen the scope one category at a time. Each step is based on what the review data shows, not on how confident anyone feels.

Keep sampling even after a category runs automatically. Checking a small share of completed cases now and then catches slow drift, such as a supplier changing their document layout or a new kind of request appearing, before it turns into a problem.

How to measure a human in the loop workflow

A few simple measures tell you whether the balance is right. Track the share of cases that go straight through, the share that go to review, and how often reviewers change what the automation proposed. Add how long cases wait in the queue.

If reviewers rarely change anything in a category, that category may be ready for less review. If they often correct the same field, that points to a specific fix. If the queue keeps growing, the review step itself needs attention, whether that is better tools, clearer ownership or fewer cases sent for review.

How Walhallah designs automations with people in the loop

Review steps are part of how we build every automation, not an afterthought. We agree with you which actions are safe to automate, which need a check and which should always stay with a person, and we build the review screen so that checking a case takes seconds rather than minutes.

We start small: one process, your own examples and a focused prototype, often in days. A production version that connects to your tools and includes the review flow usually follows in weeks rather than months. From there, the review data shows where to automate further and where people should keep the final word.