Review is part of the operating design
An AI agent can prepare useful work while still needing a person to check an important decision. That is a practical way to use its strengths. The system can gather information, organize a request, and propose a next step, while an employee applies context that may be incomplete, sensitive, or difficult to represent in instructions.
NIST's AI Risk Management Framework calls for defined responsibilities in human and AI arrangements and for ongoing evaluation. For a business owner, this points to a concrete design question: which person is responsible for which decision, and what does that person need to evaluate it properly? Adding an approval button is only the beginning of answering that question.
Put review at meaningful boundaries
Some tasks are easy to reverse and verify, such as preparing an internal draft. Other actions affect customers, records, or commitments. A hypothetical sales assistant might summarize a conversation and draft follow-up tasks automatically, while a salesperson checks the proposal and approves any customer-facing message. The review boundary follows the consequence of the action.
Choose checkpoints based on the work instead of requiring the same review everywhere. Too many low-value approvals can create a habit of clicking through. Too few can leave an employee unaware of actions that require their authority. Start conservatively for an unfamiliar workflow and use evidence from real testing to decide where a simpler review arrangement is justified.
Give the reviewer something they can assess
Present the proposed action, the facts it relies on, and any missing information together. If an agent prepares a customer reply, the reviewer should see the relevant request and source guidance. If it proposes changing a record, show the current value and proposed value. Review becomes more meaningful when the person can inspect the decision without reconstructing the entire process.
Allow the reviewer to correct, reject, or request more information. Avoid designs where approval is the only convenient path forward. Make the decision's scope clear so a person approving a draft does not accidentally authorize additional downstream actions. Record what was approved and preserve enough context to explain the outcome later, while respecting the sensitivity of the underlying information.
Evaluate the review process itself
Measure review time, corrections, rejected proposals, and mistakes that survive approval. Frequent corrections may indicate weak instructions, poor source material, or a task that needs narrower scope. Very fast approvals may indicate easy work, but they may also mean the reviewer lacks time or context. Discuss the pattern with the people involved rather than assuming the numbers explain themselves.
Give reviewers training in the specific errors the system can make, including plausible unsupported details and missed exceptions. Revisit responsibilities when the workflow changes. A good review process lets AI do useful preparation and gives people a manageable decision to make. The business gains assistance without blurring who is accountable for actions that matter.
Choose review points by consequence. Show the proposed action and supporting facts together, and measure whether review catches meaningful errors without creating avoidable delay.
Sources & further reading
Primary references for the concepts discussed here. Any business examples in this article are illustrative; results depend on your process, tools, and implementation.