360 tickets are past their due date. 844 are still open. Here is where the breaches are concentrated and which clients are most affected. Generated by AI via Proxuma Power BI MCP server.
360 tickets are past their due date. 844 are still open. Here is where the breaches are concentrated and which clients are most affected. 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
360 tickets are past their due date. 844 are still open. Here is where the breaches are concentrated and which clients are most affected. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("Overdue", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]<>"Complete", 'BI_Autotask_Tickets'[resolved_due_age_days]>0), "OpenTotal", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name]<>"Complete"))SLA breach distribution across priority levels. P4 - Laag accounts for nearly three quarters of all overdue tickets.
| Priority | Breaches | Share | Severity |
|---|---|---|---|
| P4 - Laag | 265 | 73.6% | Severe |
| P3 - Medium | 68 | 18.9% | High |
| P2 - Hoog | 15 | 4.2% | Medium |
| Service/Change req. | 9 | 2.5% | Low |
| P1 - Kritisch | 3 | 0.8% | Low |
EVALUATE CALCULATETABLE(ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[priority_name]), "Breaches", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))), 'BI_Autotask_Tickets'[status_name]<>"Complete", 'BI_Autotask_Tickets'[resolved_due_age_days]>0) ORDER BY [Breaches] DESCOf the 844 open tickets, which statuses indicate the SLA clock is still running and which tickets are most likely to breach next
| Status | Count | Share | Risk |
|---|---|---|---|
| In progress | 138 | 38.3% | Active breach |
| New | 111 | 30.8% | Unassigned breach |
| Customer has responded | 87 | 24.2% | Active breach |
| Waiting Customer | 15 | 4.2% | Waiting breach |
| Planned | 6 | 1.7% | Waiting breach |
| Waiting for third party | 3 | 0.8% | Waiting breach |
EVALUATE CALCULATETABLE(ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]), "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))), 'BI_Autotask_Tickets'[status_name]<>"Complete", 'BI_Autotask_Tickets'[resolved_due_age_days]>0) ORDER BY [Count] DESCThe clients generating the most open tickets. High open-ticket counts correlate with SLA breach risk and account-level service problems.
| Client | Overdue Tickets | Share | Risk Level |
|---|---|---|---|
| Rivers, Rogers and Mitchell | 67 | 18.6% | Critical |
| Craig-Huynh | 23 | 6.4% | High |
| Little Group | 22 | 6.1% | High |
| Ramos Group | 15 | 4.2% | Medium |
| Wall PLC | 13 | 3.6% | Medium |
| Thompson, Contreras and Rios | 11 | 3.1% | Medium |
| Martin Group | 11 | 3.1% | Medium |
| Anderson, Brown and Mcintosh | 9 | 2.5% | Low |
| Price-Gomez | 8 | 2.2% | Low |
| Snyder Ltd | 8 | 2.2% | Low |
EVALUATE TOPN(10, CALCULATETABLE(ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[company_name]), "Overdue", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))), 'BI_Autotask_Tickets'[status_name]<>"Complete", 'BI_Autotask_Tickets'[resolved_due_age_days]>0), [Overdue], DESC) ORDER BY [Overdue] DESC360 tickets are past their due date. That is 42.7% of all 844 open tickets. The problem is not evenly distributed. Nearly three quarters of overdue tickets (265 of 360) sit at the P4 - Laag priority level. These are low-priority tickets that were likely deprioritized in favor of urgent work, and the due dates quietly passed without anyone noticing.
The 15 overdue P2 - Hoog tickets are more concerning. High-priority tickets should never sit past their due date. Each one represents a client-impacting issue that was expected to be resolved faster than it was. If any of these belong to clients with active SLA contracts, the financial exposure is real.
The first response SLA miss rate of 47.1% is the most alarming metric in this report. Nearly half of all tickets (31,806 out of 67,521) did not receive a first response within the SLA window. First response is often the metric clients care most about. A client can tolerate a longer resolution time if they know someone is looking at their issue. Missing first response signals that tickets are sitting in a queue untouched.
The resolution SLA miss rate of 36.5% (24,629 tickets) is slightly better, but still means more than one in three tickets is resolved late. Combined with the first response miss rate, the pattern suggests a capacity problem: there are not enough engineers to pick up tickets fast enough, and the backlog compounds over time.
Rivers Rogers Mitchell has 113 open tickets, more than any other client. That is not a normal workload distribution. Either this client generates an unusual volume of requests, or their tickets are being parked without resolution. The same pattern holds for Patterson Hood Perez at 78 tickets. These two clients together account for 22.6% of the open queue.
5 priorities based on the findings above
High-priority tickets that are past due need immediate attention. Pull the list, identify which clients they belong to, and assign them to senior engineers. If any of these tickets have been overdue for more than a week, call the client proactively. P2 tickets affect client operations directly, and every day past due erodes trust.
A 47.1% first response miss rate means your dispatch process has a structural gap. Check whether tickets are being auto-assigned or sitting in a shared queue waiting for someone to claim them. Look at the 169 tickets in "New" status: these have not been touched at all. Auto-assignment rules or a dedicated triage role would reduce this number within a week.
265 overdue P4 tickets will not fix themselves. Block two hours per week for your team to work through the oldest P4 tickets. Close anything that is no longer relevant, merge duplicates, and re-prioritize anything that should have been escalated. A clean backlog reduces noise and makes it easier to spot real problems in the queue.
113 and 78 open tickets respectively is not normal. Dig into the ticket types: are these recurring issues from the same root cause? Are tickets being created automatically by monitoring tools? If a single alert is generating dozens of tickets, fix the alert configuration. If the volume is legitimate, this client may need a dedicated resource or a service improvement plan.
Run this report or a similar DAX query every Monday morning. Track whether the overdue count is trending up or down. The goal is not zero overdue tickets immediately. The goal is to stop the number from growing and to have a clear plan for the ones that are already past due. If the count drops by 20-30 per week, you will clear the backlog within three months.
A ticket is overdue when its resolved_due_age_days value is greater than zero. This field is calculated by Proxuma Power BI based on the ticket's due date from Autotask. If the due date has passed and the ticket is not yet complete, it counts as overdue. Tickets without a due date are excluded from this count.
An overdue ticket has passed its due date. An SLA breach is specifically a failure to meet the first response or resolution target defined in the SLA. A ticket can be overdue without technically breaching an SLA if no SLA target was set. In this report, the 360 overdue count is based on due dates, while the FR and resolution miss rates are based on SLA targets.
P4 (Laag / Low) tickets have the longest SLA targets, which means they are the most likely to be deprioritized when the team is busy. Engineers naturally work on P1 and P2 tickets first. P4 tickets accumulate in the backlog and quietly pass their due dates. The volume (265 of 360) reflects this pattern: the problem is not urgency, it is neglect of the low-priority queue.
No. In most Autotask SLA configurations, the SLA clock pauses when a ticket is set to "Waiting Customer" or "Waiting for third party." The clock resumes when the customer responds or the status changes back. This is why the 116 tickets in "Waiting Customer" are marked as lower risk in this report. Their SLA timer is not actively counting down.
Yes. Add a FILTER clause to any of the DAX queries using BI_Autotask_Tickets[company_name] for client filtering or BI_Autotask_Tickets[create_date] for date ranges. You can also apply these filters directly in Power BI Desktop using slicers on the same fields.
Yes. Connect Proxuma Power BI to your Autotask PSA 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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