4 MIN READ · PRACTICAL GUIDE
Adding an “Approve” button does not automatically create meaningful oversight. The reviewer needs the evidence, time and authority to reject a result, and the workflow needs a safe path when nobody responds.
This article proposes an operational design for routine business automation. It is not a legal classification under the EU AI Act and does not establish that a system is suitable for high-impact decisions.
Separate a suggestion from an action
Start by describing the output and its consequence. Drafting an internal summary, changing an invoice record and sending a message to a customer are different actions with different failure costs. Do not give a model permission to perform all three simply because they sit in the same workflow.
For an illustrative invoice-processing pilot, the AI component might propose a supplier name and category. A deterministic check can confirm that the supplier exists and that the required fields are present. A reviewer then resolves discrepancies. Payment authorisation should remain within the established finance controls rather than be implied by acceptance of the extracted text.
Microsoft’s AI Builder documentation shows how prompts can be included in Power Automate flows and discusses human review. Product support for a review step is useful, but the organisation still needs to decide what the reviewer is actually accountable for.
Give reviewers the evidence they need
A review screen should show the original material beside the proposed output. Highlight the fields that changed and explain which checks passed or failed. Avoid requiring the reviewer to reconstruct the full context from several different systems.
Keep the available actions clear: accept, correct, reject or request more information. Record the final decision and the responsible account. Where personal data is involved, keep only the logging necessary for the defined purpose and follow the applicable retention rules.
| Design question | A useful answer |
|---|---|
| What can AI propose? | A defined set of fields or a draft, with no independent payment authority |
| What must a person check? | The source evidence and the fields that affect the next action |
| What happens on rejection? | The task returns to a controlled queue with a reason |
| What happens on timeout? | No consequential action proceeds; the task is escalated |
| Who can override? | A named role with an auditable reason, not a shared login |
Design for the awkward cases first
Test missing documents, duplicate requests, contradictory source information and a reviewer who is unavailable. Decide whether a second attempt replaces the first or creates a new task. Without an explicit rule, an automation can accidentally cause two people to approve the same business event.
Do not treat model confidence as a universal safety threshold. A numeric score is useful only when its meaning and performance have been evaluated for the actual task. A polished answer can still be wrong. Route uncertain or unsupported cases to review rather than inventing a reliable-sounding explanation.
For European teams, include representative languages, number formats and currencies in the test set. A pilot using only clean English examples may not reveal problems in a multilingual operation. Ensure the test data is authorised and avoid putting confidential documents into an unapproved service.
Measure corrections as well as speed
Track the proportion of outputs accepted unchanged, the time spent correcting them, the number of duplicate tasks and the number of exceptions left unresolved. Compare the end-to-end task with the previous process. A faster draft is not necessarily a faster completed workflow.
Agree on stop conditions before the pilot begins. A material unauthorised action, unexplained data exposure or repeated inability to trace a result should trigger investigation, not be absorbed into an average success rate. Keep a manual fallback available until the process is proven.
The NIST AI Risk Management Framework is voluntary guidance for thinking about trustworthy AI systems. It can help structure a risk discussion, but using it does not itself demonstrate compliance with European law. The controls in this article are practical proposals rather than a substitute for a full risk assessment.
Next step
Draw the exception path for one small task before automating the normal path. Assign the reviewer, the escalation route and the conditions under which the process stops. Then run a limited pilot with observable outcomes.
For the financial side of the pilot, use the SaaS cost model and include reviewer time. For data resilience, use our recovery-drill guide.
Sources and scope
Checked 14 November 2026: Microsoft Learn: prompts in Power Automate; NIST: AI Risk Management Framework. The pilot scenario and review table are our editorial design suggestions.