Field guide

Put human judgment at the consequential handoff.

Human oversight is useful only when a person knows what to check and has the authority to change the result. These conversations offer a way to decide where review belongs as AI supports decisions and agents take action.

Use error likelihood, consequences, reversibility, and system access to decide where human review belongs in an AI workflow.

Consider both error and consequence

Vasant Dhar describes a trust map with two questions: how often is a system wrong, and what happens when it is? A low-cost, reversible suggestion may need a light check. A decision that can materially affect a customer, employee, or financial record needs a more deliberate one.

Hear the conversations: Vasant Dhar

Ask whether the decision can be undone

Rana Gujral emphasizes reversibility and the difference between genuinely deciding and merely approving AI output. For hard-to-reverse choices, record the evidence, alternatives, reviewer, and reason for the final call. Preserve opportunities for people to think without AI as well.

Hear the conversations: Rana Gujral

Set approval before an agent acts

Bryan McAnulty raises permissions, approval gates, and prompt injection when agents are assigned work. Oren Michaels highlights the risk of unsanctioned connections into company systems. Review should happen before an agent sends, changes, or commits something consequential, with access limited to the task.

Hear the conversations: Bryan McAnulty Oren Michaels

Name the accountable person

John Glasgow describes AI in finance while people remain accountable for accuracy and outcomes. Kate Bravery discusses trust and ethical responsibility in workforce decisions. In both settings, the owner of the decision should be clear even when AI performs part of the work.

Hear the conversations: John Glasgow Kate Bravery

Questions answered

What leaders ask next.

Does every AI output need manual approval?

Review should fit the chance and consequence of error. Low-risk, reversible work may use lighter checks; consequential decisions and actions need stronger review.

Hear the conversations: Vasant Dhar Rana Gujral

What should a reviewer check?

Check the evidence and context behind the output, its fit for the specific task, and the likely consequence of an error. A reviewer needs authority to correct or reject it.

Hear the conversations: Rana Gujral John Glasgow

When should an agent stop for approval?

Before it performs an action with meaningful external, financial, security, or people consequences, or crosses an access boundary that the task owner has not approved.

Hear the conversations: Bryan McAnulty Oren Michaels

Can AI weaken human judgment?

Rana Gujral warns that a team can become faster while exercising less independent thinking. Review should be real decision-making rather than an automatic sign-off.