Plan requirement
| Subscription | Suite Professional or higher, Explore Professional or higher |
| Access | Agent |
What the AI agent takes off the queue, measured in a way that survives a rise in traffic.
Build it
- Metric: conversations resolved by the AI agent, as a share of conversations handled.
- Attribute: by month, and by channel.
- Add tickets reaching agents on the same subjects, from the ticket dataset.
Share, not count
Resolutions rise when traffic rises. Only the share tells you whether the agent got better, and that is the number to put on a dashboard.
The comparison that matters
Human ticket volume on the subjects the AI agent handles, before and after it went live. That is what the business is actually asking: did work leave the queue.
Resolution counts alone do not answer it, because an AI agent can handle conversations that would never have become tickets.
Add repeat contact
Customers coming back within a day on the same subject. A high resolution share with high repeat contact means people are being sent away rather than helped, and the two numbers together are unambiguous.
Split by channel
Messaging and email behave differently enough that a combined figure describes neither. Email is where a poor answer costs most, so it deserves its own line.
Tag the tickets
If AI-handled conversations carry a tag, every future report is straightforward. Adding it costs one trigger and saves a lot of inference later.
Report it with what it cost
Resolutions are billed. The honest report puts the volume handled next to that, so the conversation about value is based on both halves.
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