> ## Documentation Index
> Fetch the complete documentation index at: https://docs.getthread.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Service Team Performance & AI Usage Dashboard
> Compare your service desk's last 30 days against the prior 30 — tickets closed, first-day resolution, reopen and single-touch rates, stale queues, and per-technician AI adoption.
The Service Team Performance & AI Usage dashboard gives service leaders a clear read on how the support team performed over the last 30 days, against the prior 30 days so trends are obvious. It pairs throughput and quality metrics with per-technician AI adoption, so you can see who's getting leverage from Thread's AI and who hasn't started.
Open it from **Thread admin → General → Analytics**.
## What this dashboard answers
* Are we **closing more** than last month, and **faster**?
* Is quality holding — **reopens**, **single-touch resolutions**, **first-day resolution**?
* Whose **queue is going stale**?
* Who are our **top performers**, on volume and on quality?
* Which technicians have **adopted Super Magic**, and which haven't touched it?
## Filters and time windows
| Window | Where it's used |
| ----------------------- | ------------------------------------------------ |
| Last 30 days | Every section |
| Prior 30 days | The comparison column and percent-change figures |
| Per-technician all time | First and last AI use dates |
## What's on the dashboard
### Service team performance summary
A team-wide scorecard comparing the current period against the previous one:
* Tickets closed and tickets touched, each with percent change
* First-day resolution rate
* Reopen rate
* Single-touch resolution rate
* Average response and resolution time
* Share of tickets touched by Magic AI and by Magic Agents
* Reassignment rate
### Service team performance scorecard
A per-technician breakdown showing, for each team member: tickets closed and touched, distinct customers helped, median response and resolution time, first-day resolution rate, reopen rate, average customer sentiment, average touches per closed ticket, active days, reassignments given and received, and how many tickets in their queue are going stale.
### Member Super Magic usage and insights
How individual technicians used Thread's AI over the past 30 days — including Super Magic and AI-assisted time entry — with first and last use dates, so adoption and abandonment are both visible.
### Member AI usage with trend
A period-over-period view of AI adoption per technician: Super Magic usage, AI time entry usage, and total AI usage, each with the change from the prior period, plus a count of active AI users.
## Metric definitions
| Metric | Definition |
| ---------------------------- | ---------------------------------------------------------------------------------------------------- |
| Tickets closed | Tickets the technician closed in the period. |
| Tickets touched | Tickets the technician worked on, whether or not they closed them. |
| First-day resolution rate | The share of tickets resolved on the day they arrived. |
| Reopen rate | How often closed tickets come back. |
| Single-touch resolution rate | The share of tickets solved in one interaction. |
| Touches per closed ticket | Average interactions needed to close — the inverse read on single-touch. |
| Reassignment rate | How often tickets move between technicians. |
| Stale tickets | Tickets with no update in 24 hours, counted from both an internal and a customer-facing perspective. |
| Active days | Days in the period on which the technician did any work. |
| AI leverage | The share of a technician's tickets touched by Magic AI or Magic Agents. |
## Things to watch
**This dashboard does not exclude automated or system-generated tickets.** If your boards include monitoring or alerting queues, that volume is counted here — so filter or exclude those boards before you use this as a purely human performance view.
* **Closures made directly in your PSA can't be credited to a technician.** A PSA API limitation around impersonation means only closures made in Thread Inbox or Pods attribute to a member. Read closer metrics as a view of closures happening in Thread. See [*Things to know & gotchas*](/analytics/things-to-know-and-gotchas).
* **Volume without quality is a trap.** Read tickets closed next to reopen rate and touches per closed ticket before naming a top performer.
* **Stale counts are the most operationally useful column** in the scorecard — they surface a queue problem before it becomes an escalation.
* **Reassignments given and received tell different stories.** High received can mean a specialist; high given can mean triage is misrouting.
* **Data refreshes daily**, so today's closures generally appear tomorrow.
## Next steps
Turn the scorecard into a one-on-one.
The service manager's operating rhythm around these numbers.
## Related topics
- [Getting Started with Magic Analytics](/analytics/getting-started-with-magic-analytics.md)
- [Super Magic Usage Dashboard](/analytics/super-magic-usage-dashboard.md)
- [Magic Analytics Topics You Can Explore](/analytics/topics-you-can-explore.md)