The useful question for a business AI project is what happens when its output is wrong. A draft note, an external message and a decision that affects someone’s access to a service carry different consequences. Design the review process around those consequences before choosing how much authority to give the system.
Separate Assistance From Action
List the proposed uses and the actions each could trigger. Drafting a summary for an employee to check is different from sending that summary to a customer or changing a record automatically. Start with a narrow task whose result a knowledgeable person can verify.
NIST’s Generative AI Profile identifies confidently stated false output as a risk. That makes fluent wording a poor acceptance criterion. A reviewer needs the underlying evidence and a clear way to correct or reject the result.
Give The Reviewer A Real Decision
Define what must be checked: factual details, missing context, inappropriate recommendations or changes in meaning. Show the source information alongside the proposed output when access is appropriate. Require an active decision before consequential external action.
Assign enough review time and authority to stop the process. If staff are expected to approve a large queue faster than they can inspect it, a review button provides little practical control. Measure the work required to review and correct outputs as part of the project’s economics.
Evaluate The Difficult Cases
Create an approved evaluation set containing ordinary examples and predictable failures. Include incomplete information, contradictory statements, ambiguous wording and requests outside the intended scope. Use fictional or otherwise authorized data.
For an illustrative call-summary tool, evaluate whether it invents a commitment, attributes a statement to the wrong speaker or omits the customer’s unresolved question. Count material errors separately from minor style changes. Define the error level and kinds of failure that require suspension before testing begins.
Control Information And Permissions
Review the data the provider receives, applicable agreements, access controls, retention and the system’s ability to take actions. Give it only the access needed for the approved task. Keep professional judgments, such as clinical, legal or insurance advice, with qualified people through an explicitly approved process.
Document who can change the instructions, approved knowledge or connected tools. Re-evaluate behavior when those inputs change. An earlier test does not establish that a revised configuration will behave the same way.
Plan The Stop And Recovery Path
Decide how to disable the workflow, identify affected records and correct a wrong external action. Preserve appropriate review evidence without filling logs with sensitive material. Assign an owner who remains responsible after the pilot.
The three possible decisions are expand, revise or stop. VG can help define the workflow and evidence for that decision. An AI feature should earn its place through useful, reviewable work and manageable errors; its presence alone is not a growth outcome.
Common Questions
Does Human Review Make Any AI Workflow Safe?
No. Review must be informed, timely and supported by appropriate permissions and recovery controls. Some proposed tasks remain unsuitable for automation even when a person can inspect the result.
What Is A Sensible First Use Case?
Consider a limited internal drafting or organizing task with accessible source material, clear evaluation criteria and an accountable reviewer. The right choice depends on your data and operating context.
Sources And Further Reading
- NIST: Generative Artificial Intelligence Profile, AI 600-1 · Checked 2026-10-21
These sources provide context. Requirements depend on the actual activity and applicable jurisdiction. Business-process guidance does not replace professional advice.