What happens when AI produces more than people can review?

AI makes it much easier to produce more work. That creates an important question for the people expected to review it.

If a team can generate documents, code or customer responses much faster than before, the review process may struggle to keep up. We can add more reviewers, but there is a limit to how much material people can assess carefully. When the volume becomes too high, there is a risk that review turns into a routine approval rather than a meaningful check.

Human oversight still matters. The question is where it adds the most value and how we design the surrounding process to support it.

A useful place to start is the decision being made and the impact of getting it wrong. Helping someone explore holiday ideas carries a different level of consequence from confirming a booking, charging a customer or changing their access to a benefit.

Those situations may need different controls. Some activity might be suitable for automated checks and sampling. Other decisions may need a person to review an exception or approve an action before it happens. In each case, someone needs to understand what the system is permitted to do and who is accountable for the outcome.

We also need to look at the whole workflow. If AI speeds up one stage but leaves the next team with a much larger queue, the work hasn’t necessarily moved through the organisation any faster. The bottleneck has moved.

Good governance should help teams make these decisions early. Clear boundaries, testing, monitoring and a route to escalate problems give people more confidence to use a capability. They also help reviewers focus their attention where their judgement is most needed.

As AI becomes part of everyday work, we need to design oversight for the volume we expect to create, not assume that the review process we have today will scale with it.