Support Tickets

What people struggle with, in their own words

Support tickets are the closest thing most teams have to their users speaking in their own words, and they are almost never used that way.

A support ticket is a small, structured piece of evidence. Someone had a problem, tried to describe it, and submitted it. The language they used, the thing they were trying to do, the part that confused them: all of that is there, in their own words, not filtered through a product manager's summary or a user researcher's report. It is one of the most direct signals a team receives, and it typically lives in a dedicated support tool that no one outside the support function ever reads systematically.

For an AI, a corpus of support tickets is an unusually rich source of context. It reveals which features cause confusion, what language users actually use to describe their problems (which is often not the language the team uses), and where the gap between how something was designed and how it is actually experienced is widest. None of that reaches the people who could act on it unless someone exports it and presents it.

The readiness question here has two parts: whether the tickets are being captured in a way that makes them searchable and useful, and whether there is any route from the ticket corpus to the people and tools that could act on what it reveals.

Make it visibleExport your last three months of closed tickets (or a sample of 50-100) and sort them by the simplest categories: what part of the product or service they relate to, and whether the issue recurred. Put that sorted export in a shared folder your AI tool can read.

Why AI needs this

Each part of your work matters to AI in a specific way. Some of it is context a tool needs before it can help, some of it is work a tool can take on, and some of it is judgment that should stay with you.

User language

Tickets use the words real users reach for when something is not working. A tool that can read a corpus of tickets can help you match that language in documentation, error messages, and onboarding, closing a gap that formal research rarely surfaces.

Recurring issues

A pattern across tickets is stronger evidence than a single complaint. Grouping and labelling tickets by theme, then making that grouping accessible to a tool, is one of the most direct ways to connect AI to user reality.

Unmediated signal

Unlike survey responses, tickets arrive without a researcher framing the question. The friction is real, the language is raw, and the context is specific. This unmediated quality is exactly what makes them valuable as input to an AI.

Resolution knowledge

The answers written to repeat questions are themselves a knowledge base. A tool that can access both the question and the resolution can draft accurate first responses, but only if that pairing is captured and kept.

Questions to explore

Use these on your own or in a group. There are no right answers, only better conversations.

How often does the team outside the support function read through recent tickets to understand what users are experiencing?
What is the most common issue in your tickets right now, and how would you know if it had been resolved?
If an AI were to read your last hundred tickets, what do you think it would learn about your product that the team does not know explicitly?
Where do closed tickets go, and can anyone access them to look for patterns months after they were resolved?
How does the language in your tickets compare to the language in your documentation and marketing?

Readiness traps

Tickets contain customer-identifying information by default. Any workflow that routes ticket content to an AI tool needs to account for data privacy, and in some jurisdictions this requires explicit consent or anonymisation.
Tickets that go untagged or uncategorised become a large, unstructured pile that is hard to analyse. Consistent tagging from the start makes the corpus far more useful.
Support tickets reflect who reaches out for help, not the full population of users. They are a valuable but biased signal: the most confused users and the most motivated ones are over-represented, and the silent majority is not in the data.