Governance

Design human review into your AI workflow

Decide which AI actions need approval, who owns exceptions and what context a reviewer needs to make a useful decision.

Uniforce editorial teamPublished Updated 2 min read
A digital coworker protected by a shield and human-controlled access permissions

Map decisions before automating actions

AI workflow governance starts with the decisions inside the work. A process may include gathering information, preparing a response, changing a record and making an external commitment. Those actions do not all need the same level of review.

Walk through a real case with the people who handle it today. Mark where a mistake would be easy to correct and where it could affect a customer, another team or a financial commitment. Use those distinctions to design the review points.

Make approval rules specific

A rule such as ‘ask when unsure’ is difficult to evaluate on its own. Add observable triggers: missing customer identity, conflicting records, an action outside the role’s scope or a request for a policy exception. Define what the workflow should do while it waits.

For example, a service workflow could prepare a response using an approved policy but route an exception to the service lead. The digital worker gathers the facts; the person decides whether to make the commitment.

Give the reviewer a decision-ready handoff

A useful approval request explains what was asked, which information was used, what action is proposed and why it needs a person. It should make missing or conflicting information visible instead of hiding it in a confident summary.

Assign an owner for the review queue and a fallback when that person is unavailable. Otherwise, the approval step becomes a waiting room with no clear responsibility for the customer’s next update.

Review exceptions as part of operations

Track the kinds of cases that reach a person and what the reviewer changes. Repeated corrections may point to an unclear policy, missing context or a workflow that is broader than the current role can support.

Update the playbook deliberately and retest representative cases. The goal is not to eliminate human involvement. It is to place human attention where it improves the outcome, with enough context to make that attention useful.

About this guide

Prepared by the Uniforce editorial team. Examples illustrate proposed workflows, not customer results. The role’s access, actions and review requirements depend on the configured implementation.

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