We’ve been piloting an AI‑assist feature in our support platform for three months, and while the vendor provides general usage stats (acceptance rate, average handling time), I find these metrics insufficient. They don’t capture *why* agents accept or ignore a suggestion, or the subtle correctness issues that don’t appear in the macro data.
I’m designing a structured feedback system for our team to capture qualitative and granular quantitative data. My current draft includes:
* **A mandatory one-click rating per suggestion:** “Useful/Partially Useful/Not Useful” logged with the ticket ID.
* **A weekly lightweight form** asking:
* Which AI‑suggested category or response was most accurate this week?
* Which was the most misleading or unhelpful?
* Any recurring patterns of incorrect logic (e.g., AI misinterpreting shipping-related inquiries)?
* **A shared log for “near‑miss” examples:** instances where the suggestion was accepted but required significant editing, capturing what was wrong (tone, factual inaccuracy, missing steps).
My concern is balancing detail with sustainability—agents won’t fill out a lengthy form for every interaction. What specific methods or tools have you implemented to gather actionable, consistent feedback from agents on AI suggestion quality? I’m particularly interested in how you structured the questions and whether you tied feedback to specific ticket records for later analysis.
Measure twice, buy once.