844 tickets are open. This report shows how they split across statuses, which are actionable, which are waiting, and where the biggest concentrations sit by client. Generated by AI via Proxuma Power BI MCP server.
844 tickets are open. This report shows how they split across statuses, which are actionable, which are waiting, and where the biggest concentrations sit by client. Generated by AI via Proxuma Power BI MCP server.
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Service desk managers, dispatch leads, and operations teams
How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning
844 tickets are open. This report shows how they split across statuses, which are actionable, which are waiting, and where the biggest concentrations sit by client. Generated by AI via Proxuma Power BI MCP server.
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[status_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'))All 844 open tickets grouped by their current status, ranked by count
| # | Status | Count | % of Open | Share |
|---|---|---|---|---|
| 1 | Planned | 213 | 25.2% | |
| 2 | In progress | 205 | 24.3% | |
| 3 | New | 169 | 20.0% | |
| 4 | Waiting Customer | 116 | 13.7% | |
| 5 | Customer has responded | 102 | 12.1% | |
| 6 | Waiting for third party | 38 | 4.5% | |
| 7 | Assigned | 1 | 0.1% |
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[status]),
"count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
)
ORDER BY [count] DESCOpen tickets split into two categories: tickets your team can work on right now, and tickets blocked by external dependencies
| Category | Statuses included | Count | % of Open |
|---|---|---|---|
| Actionable | New (169) + Assigned (1) + In progress (205) + Customer has responded (102) | 477 | 56.5% |
| Waiting | Planned (213) + Waiting Customer (116) + Waiting for third party (38) | 367 | 43.5% |
EVALUATE
ROW(
"Actionable", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[status] IN {"New","Assigned","In progress","Customer has responded"},
'BI_Autotask_Tickets'[status] <> "Complete"),
"Waiting", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[status] IN {"Planned","Waiting Customer","Waiting for third party"},
'BI_Autotask_Tickets'[status] <> "Complete")
)Clients holding the most open tickets right now. High concentrations can indicate ongoing issues, large projects, or clients that need a dedicated review.
EVALUATE
TOPN(
5,
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[company_name]),
"OpenCount", CALCULATE(
COUNTROWS('BI_Autotask_Tickets'),
'BI_Autotask_Tickets'[status] <> "Complete")
),
[OpenCount], DESC
)
ORDER BY [OpenCount] DESCThe overall open rate of 1.25% (844 out of 67,521 total tickets) is low, which indicates tickets are generally being closed. The concern is not volume. The concern is what is happening inside that 844.
360 of those 844 tickets are past their SLA deadline. That is 42.7% of the open backlog. Even if many of those are low-priority or informational, the number is high enough that it will show up in client reporting. SLA overdue is a lagging indicator: by the time a ticket crosses the line, the client has already been waiting too long.
The status distribution shows that Planned (213) is the single largest status bucket. These are tickets that have been acknowledged and scheduled but are not being actively worked. If many of those planned tickets have been sitting for weeks, the "planned" label is doing more to hide a backlog than to manage one.
169 tickets in "New" status have not been touched at all. Each one is waiting for someone to assign it, acknowledge it, or start working on it. For a team running on SLAs, untouched tickets are the fastest path to a breach. The fact that "New" is the third-largest status group suggests triage is falling behind.
On the client side, Rivers Rogers Mitchell holds 113 open tickets, which is 13.4% of the total. Patterson Hood Perez follows with 78. Together, the top five clients account for 39.2% of all open work. Whether this reflects large projects, recurring issues, or a client that generates more tickets than others, it is worth checking whether these accounts are getting proportional attention from your team.
5 priorities based on the findings above
Every ticket in "New" status is waiting for someone to pick it up. Sort them by age, assign them to the right queue or technician, and close any that are duplicates or already resolved. 169 untouched tickets is a gap in your dispatch process, and the longer they sit, the more likely they are to breach SLA.
42.7% of your open backlog is past the SLA deadline. Some of those may be low-priority informational tickets or tickets where the SLA clock should have been paused. Pull the list, filter by priority and queue, and identify which ones are genuine breaches versus configuration issues. The number is high enough to affect your reported SLA compliance rate.
Planned is the largest status bucket. That is fine if these tickets are genuinely scheduled for upcoming work. It is a problem if they have been sitting in "Planned" for weeks without movement. Filter by last-updated date. Any ticket that has been in "Planned" for more than two weeks without a note should be re-evaluated or moved to a more honest status.
113 open tickets for a single client is unusual. Check whether these are project tickets (expected), a backlog of unresolved service issues (a problem), or tickets that should have been closed (a hygiene issue). If this client consistently sits at the top of the open-ticket list, consider assigning a dedicated resource or escalating with their account manager.
These tickets were waiting on the customer, and the customer has replied. Every one of them is now waiting on your team to pick it back up. Letting these sit creates a bad experience: the customer did their part and is now watching the clock. 102 tickets in this state means your team should be checking the "Customer has responded" queue at least once per day.
Any ticket in Autotask whose status is not "Complete." This includes New, Assigned, In progress, Planned, Waiting Customer, Waiting for third party, and Customer has responded. The report counts all non-complete tickets at the time the query was run.
The Proxuma Power BI model tracks a field called resolved_due_age_days. When this value is greater than zero, the ticket has exceeded its SLA resolution target. The 360 overdue count includes all open tickets where the SLA deadline has passed, regardless of priority level.
Actionable tickets are in a status where your team can work on them right now: New, Assigned, In progress, or Customer has responded. Waiting tickets are blocked by someone else: the customer (Waiting Customer), a vendor (Waiting for third party), or scheduled future work (Planned). The split helps you understand how much of the backlog is within your control.
Planned tickets are scheduled for future work. They are not actively being worked on today. Categorizing them as "Waiting" reflects their current state: the ticket exists, but no one is progressing it right now. If you want to count Planned as actionable for your team, you can adjust the DAX query grouping.
Yes. Add a filter on the queue_name or resource_name column in the DAX queries. For example, add a filter for 'BI_Autotask_Tickets'[queue_name] = "Tier 1" to limit results to a specific queue. The Proxuma Power BI model supports filtering by queue, technician, priority, and company.
Yes. Connect Proxuma Power BI to your Autotask account, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI writes the DAX queries, runs them against your real data, and produces a report like this in under fifteen minutes.
Connect Proxuma Power BI to your PSA, RMM, and M365 environment, use an MCP-compatible AI to ask questions, and generate custom reports - in minutes, not days.
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