AI-generated content needs review for the same reasons other client content does: an inaccurate claim, misleading image or unsuitable tone can cause harm. Review effort should reflect the subject and intended use. Identify the risks in the brief before generating a large batch, rather than assuming fluent output is safe to publish.
Make the checks specific enough for a reviewer to apply. Preferred terms, prohibited claims, source requirements and escalation conditions belong in the relevant client’s rules. The brand voice review guide explains a possible approach, but no automated score establishes that all checks passed. Verify the current behavior of any review tool and retain human ownership of the approval decision.
Numerical claims need traceable evidence, and sensitive subjects may require an appropriately qualified reviewer. Set those review requirements with the client instead of assuming a general model can satisfy every industry or jurisdiction. Check named competitor references, product descriptions and permissions as relevant to the brief. If the necessary evidence or review is unavailable, hold or revise the content.
Keep the approved asset version, reviewer and decision connected. An edit after approval may change the claim, meaning or permissions, so define when another review is needed. In white-label work, be clear about which organization owns each check. Branding does not transfer responsibility away from the people authorizing publication.
Place these decisions in the production workflow and verify that the tools can enforce the boundaries you need. Bound revision attempts, keep rejected content from proceeding and define a manual path for uncertain cases. Assess examples of what the checks missed as well as what they flagged. A quiet incident log is not proof that guardrails are complete or that future incidents cannot occur.
Max Socials Team
Insights from the Max Socials product, engineering, and strategy teams.