The exhaust is the asset
An AI feature in production generates the most valuable dataset your company will ever have about its own work: real questions in customers’ words, the system’s attempt, and a human’s correction. Teams that discard it rebuild the same knowledge from scratch every time they improve the feature.
Corrections are labelled training data
Every time a person edits a draft or overrides a classification, they have labelled an example for free. Capturing that as a structured event — not a general update — is the same discipline as instrumenting software so AI can be added later, applied to the AI itself.
Unanswered questions are a product roadmap
The questions the system couldn’t resolve are a ranked list of what your documentation is missing and, often, what your product is missing. This is the cheapest research your company has access to and almost nobody reads it.
The questions your AI couldn’t answer are a better roadmap than most customer surveys.
It also feeds your public content
Real questions, in the phrasing real buyers use, are exactly what an FAQ built from genuine questions needs. The support queue and the content calendar should be the same input, read twice.
Retention needs a decision, not a default
Conversation logs contain whatever customers typed, which frequently includes personal and commercially sensitive information. Decide the retention window, what gets redacted, and who can query it before you have three years of it — retrofitting that is far harder than setting it.
Review it on a cadence
Sample fifty interactions monthly and read them properly. Dashboards show you the rate of things; reading the actual exchanges shows you what is going wrong, which is the input that changes what you build next.