Human review is a practical business control, not a formality added after AI-assisted work is complete. It gives teams a clear point to check context, confirm intent, and decide whether a draft, summary, recommendation, or routed request is ready for the next step.
Place review before meaningful action
The safest review point is before an output affects a customer, account, access request, financial decision, hiring process, legal question, or public message. AI can help prepare material for those moments, but a person should confirm the final action when the outcome carries business impact.
That review should be easy to find in the workflow. Name the owner, define what they check, and make the decision visible enough that the next person understands whether the work is approved, returned for edits, or escalated.
Use approvals to create accountability
Approvals are useful when they connect a request to an accountable owner and a clear decision record. The approver does not need to rework every draft, but they should be able to see the business purpose, the source context, the proposed outcome, and any limits that were applied.
A good approval step answers practical questions: who reviewed this work, what did they approve, what changed before approval, and which team owns the next action? Those answers help AI-assisted work stay coordinated without implying that judgment has been handed off to automation.
Escalate uncertain or high-impact cases
Escalation belongs in the workflow before people are forced to improvise. If a request is unclear, conflicts with policy, involves sensitive information, affects access, or could change a customer-facing commitment, the team should know where it goes next.
The escalation path can be simple: pause the workflow, add context, route it to the responsible team, and record the decision once it is reviewed. Clear escalation helps teams use AI support while keeping important decisions with people who understand the risk.
Automate support work, not final judgment
Safe automation is most useful around the edges of review. It can route requests, prepare summaries, remind owners, flag missing information, and format a draft for human review. Those steps reduce coordination effort without turning AI output into an automatic business decision.
Teams should be especially careful with workflows that change access, approve spend, send external messages, or affect a person's opportunity. In those cases, automation can organize the work, while accountable judgment stays with the reviewer.
Use examples that fit everyday work
A support team might use AI to summarize a customer question, then require a reviewer before the answer is sent. An operations team might use it to classify an intake request, then route unclear cases to a manager. An access request might collect context, but approval still follows the normal reviewed path.
These examples keep the promise realistic. AI-assisted workflows can make work easier to prepare, route, and review, but they should not be presented as a replacement for every human decision.