Most support dashboards were designed to manage a room full of human agents: how many tickets each person closes, how fast, how many the bot kept away from them. When an AI agent is doing the resolving, several of those numbers stop meaning what they used to — and a couple become actively misleading.
The metrics that lie now
- Containment / deflection rate. In a human world, "kept away from an agent" roughly meant "self-served successfully." With a bot in the loop, it can just as easily mean "gave up." A high containment number can hide a pile of unresolved, quietly churning customers. We've argued the whole case in resolution, not deflection.
- Average handle time. Faster was better when a human's minute was the scarce resource. An AI agent that "handles" a conversation in nine seconds by not solving anything is optimizing the wrong variable. Speed only matters given resolution.
- Ticket volume closed. Closing isn't resolving. A reopened ticket is a resolution that didn't happen, counted as one that did.
The metrics that actually matter
- Resolution rate. The share of conversations where the customer's goal was truly accomplished. This is the north star; almost everything else is a proxy for it.
- Repeat-contact rate. Did the same customer come back about the same problem within a few days? The cleanest real-world signal that a "resolution" was one.
- First-contact resolution. Solved in one interaction, no bouncing. Still one of the strongest drivers of satisfaction there is.
- CSAT, tied to outcome. Satisfaction is only meaningful when you can connect it to what actually happened in the conversation — resolved-and-happy is the goal; resolved-and-annoyed is a design problem.
- Escalation quality. Not just how often you hand off, but whether those handoffs are warm and land in the right place.
Measure the tail, not just the average
Averages are comfortable and dangerous. A 92% resolution rate feels great until you look at the 8% — because that tail is where the refunds, the complaints, and the churn live. The teams that get the most from an AI agent watch the failures as closely as the wins, because every unresolved cluster is a concrete thing to fix.
That's the shift AI forces on the metrics conversation: from activity (how much did we do) to outcomes (did the customer leave with their problem solved). Build your dashboard around the second question. The insights surface is designed to answer it.