What comes after the AI demo?

A demo can show that a model can handle an example. The next step is deciding how that capability fits into a real job.

Consider a support workflow. The agent reads a request and drafts a reply from approved help material. Now give it incomplete account details, a disputed charge, or a question that depends on something outside its knowledge. Those situations need a decision about what happens next.

I would start by writing down the smallest useful responsibility: what comes in, what the system should produce, and where a person takes over. “Help with support” leaves too much open. “Draft an answer to a routine account-access question and pass uncertain cases to the support team” gives the team something concrete to test.

Then I would inspect the handoff. Does the reviewer have the original request, the relevant source, and an explanation of what needs a decision? If they have to reconstruct the entire task, the automation has moved work around without resolving it.

The same questions apply after the first release. Someone needs to own the instructions, review failures, and decide when the workflow should change. A small set of representative examples can help the team notice regressions.

The question I like before a build is: Which part of this job should the system handle first, and what should happen when it cannot finish?

That answer gives the product a boundary. It also gives the people around it a way to learn whether the system is useful.