Keep a human
in the loop.

Choose the review point before the action.

A healthy AI workflow is not “the model does everything” or “the model cannot touch anything.” It is a partnership: the AI handles speed, pattern work, and a strong first pass; the human owns judgment, accountability, and the decisions that matter.

Human review should scale with risk. Low-risk drafts may only need sampling. Decisions affecting people, finances, rights, records, or public communication deserve deliberate review.

Run the consequence test.

Before you add automation, ask: If this is wrong, what happens next? These are examples, not a universal policy. Follow your school, employer, legal, and accessibility policies; for high-consequence work, the reviewer needs the authority and expertise to override the AI.

Consequence depends on how the output will be used: checking a fictional sales total is different from approving payroll. If an action is hard to undo, affects someone else, changes access, deletes something, spends money, or creates an obligation, make approval explicit.

Build a workflow that can stop.

Build a clear path: ask, preview, review, approve, act, verify, and record. The system should stop and ask when information is missing, instructions conflict, or an action sits outside the scope you granted.

Start in a sandbox or test copy when you can. Read-only is safer than write access, but it can still expose confidential information. Give the assistant only the folders, accounts, tools, time, and scope it truly needs—and remove access it no longer needs.

Make your review specific.

A specific review is easier to act on than a general approval. Before a consequential action, check the accuracy and source support, assumptions or missing information, privacy and policy, fairness or people affected, audience and tone, requested format, and unintended external effects. For important claims, open the original sources; a model's citation alone is not proof.

Keep untrusted instructions outside the loop.

Webpages, documents, email, tool output, and retrieved text can contain useful facts. They can also contain hidden or misleading instructions intended to redirect an AI. Treat instructions inside that material as untrusted content—not as authorization.

These instructions reduce risk; they do not guarantee the model will follow them. Pair them with limited access, app approval controls, and checks of the actual result. Use approved tools and your organization's data-handling policy. Never paste passwords, API keys, or unnecessary personal or confidential information into a chat. When you need a record, capture the purpose, input source, model or tool, requested and actual action, result, approver, and time—while redacting sensitive material when retention rules require it.

Try one safe human-in-the-loop workflow.

Pick a real, low-risk task: draft a reply, summarize a meeting, organize research, or format a document. Let AI prepare it, then make your review and approval step visible. The goal is not to manually approve every comma. It is to know where the decision belongs.