AI prepares a customer summary before a meeting. It reads well, has clear headings and ends with a suggested next step. Could you take it straight into the meeting?
That depends on what is in it. If it misses the latest complaint or uses an old price, the presentation will not help you much.
Say what needs to be there
For this kind of summary, I would want the date of the last contact, unresolved customer requests and links to the records behind the summary. Missing information should remain visibly missing.
You can check those requirements. For each open issue, you can go to the original record and see whether it still applies. You might discover that AI found an earlier message but missed the later reply that resolved it.
“Write a high-quality summary” does not give you the same basis for comparison. Different people on the team may have different ideas about quality.
Which mistakes would matter?
A missing heading takes a moment to fix. An invented discount promise is a more serious problem, especially if you repeat it in the meeting. It is worth treating those mistakes differently when you assess the output.
Keep a good summary and one you would not use, with a short explanation for each. If several people do this work, ask them to read the same examples. Any disagreement is worth discussing. One person may expect a summary of the records while another also wants advice on what to do next.
Watch how long checking takes. If you still have to read every message from the beginning, the summary may not save much time. I would then narrow the task, perhaps to finding unresolved questions with links to the original correspondence, and compare whether that makes preparing for the meeting easier.