The short answer
AI features tend to reduce simple questions and increase complex ones. The overall ticket count may fall while the time per ticket rises.
Prepare for that shift before launch rather than discovering it in the queue.
How the mix changes
| Before | After |
|---|---|
| How do I do this | Fewer, the feature handles it |
| Where is this setting | Fewer |
| It gave the wrong answer | New, and slow to resolve |
| Why is it different each time | New, needs explanation |
| It refused to help | New, needs checking |
The bottom three are all new categories requiring investigation rather than an answer, which is why average handling time rises.
Prepare support before launch
- Give them a tool to look up and replay a specific request.
- Explain how the feature works well enough for them to answer confidently.
- Agree what counts as a defect and what is expected variation.
- Agree what they escalate and to whom.
- Write the standard explanations in advance.
Point one is the difference between a ticket resolved in minutes and one that becomes an engineering request.
Expect your best customers to complain most
The customers who use a feature heavily encounter its limitations most often. Those tickets are valuable rather than a problem, and the tone of the response matters.
Treating detailed criticism from an engaged customer as a nuisance is how you lose the people most invested in the product.
Route the failure data back
- Categorise wrong-output tickets by failure type
- Add the clearest cases to the evaluation set
- Look for clusters pointing at one weakness
- Report those clusters to product regularly
- Tell the customer when their case is fixed
The last point costs a minute and is disproportionately effective. Customers who see their report acted on keep reporting.