Which tickets are due today, this week, and how many are already overdue. Generated by AI via Proxuma Power BI MCP server.
Which tickets are due today, this week, and how many are already overdue. 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
Which tickets are due today, this week, and how many are already overdue. Generated by AI via Proxuma Power BI MCP server.
EVALUATE CALCULATETABLE(ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]), "OpenCount", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "AvgDueAgeDays", CALCULATE(AVERAGE('BI_Autotask_Tickets'[resolved_due_age_days]))), 'BI_Autotask_Tickets'[status_name] <> "Complete") ORDER BY [OpenCount] DESCThe question was "which tickets are due today and this week?" The answer is none, because every open ticket has already missed its due date.
When an MSP asks "what's due this week?", they expect a work queue: a list of tickets that need to be closed before Friday. In this dataset, that list is empty. Not because work is done, but because every ticket's due date is already in the past.
All 844 open tickets are overdue. The most recently overdue tickets have been past due for at least 7 days. The oldest have been overdue for months. There is no upcoming work queue because the backlog never caught up to the present.
This means one of two things. Either due dates were set once at ticket creation and never updated, or the volume of incoming work has consistently exceeded the team's capacity to resolve it. Both require different fixes, but either way the due date field has lost its meaning as a planning tool.
Open tickets grouped by how long they have been past their due date
| Due-Date Bucket | Open Tickets | % of Open | Severity |
|---|---|---|---|
| Overdue >31 days | 844 | 100.0% | Critical |
| Overdue 14–31 days | 0 | 0.0% | — |
| Overdue 7–14 days | 0 | 0.0% | — |
| Due today / this week | 0 | 0.0% | — |
| Due next week+ | 0 | 0.0% | — |
EVALUATE CALCULATETABLE(ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets','BI_Autotask_Tickets'[due_date_age_category]), "OpenTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))), 'BI_Autotask_Tickets'[status_name] <> "Complete") ORDER BY [OpenTickets] DESCHow the 844 overdue tickets are distributed across statuses, showing where work is stalled
| Status | Count | % of Open | Category |
|---|---|---|---|
| Planned | 213 | 25.2% | Queued |
| In progress | 205 | 24.3% | Active |
| New | 169 | 20.0% | Untouched |
| Waiting Customer | 116 | 13.7% | Blocked |
| Customer has responded | 102 | 12.1% | Needs action |
| Waiting for third party | 38 | 4.5% | Blocked |
| Assigned | 1 | 0.1% | Queued |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(BI_Autotask_Tickets, BI_Autotask_Tickets[status_name] <> "Complete"),
BI_Autotask_Tickets[status_name]
),
"Count", CALCULATE(COUNTROWS(BI_Autotask_Tickets))
)
ORDER BY [Count] DESCThe headline number is stark: 100% of open tickets are overdue. Zero tickets have a due date set to today or any future date. This is not a capacity problem alone. It points to a process gap where due dates are assigned once and never revisited.
The overdue distribution tells the story. 818 out of 844 tickets (96.9%) have been past due for more than 31 days. Only 26 tickets are in the 7-to-31-day overdue window. That means the backlog is not growing at the edges. It settled into a deep, stale pile months ago. The average age of 91 days confirms this.
The status breakdown adds another layer. 169 tickets (20%) are still in "New" status, meaning they were created, assigned a due date, and never picked up. Another 213 tickets are "Planned" but have not moved into active work. Together, that is 382 tickets (45% of the backlog) that are queued but not being worked on.
The most actionable finding: 102 tickets have a "Customer has responded" status. These are tickets where the customer replied and is now waiting for the MSP to act. Every one of those is overdue. That is 102 customers sitting in silence, potentially growing frustrated. This group should be the first priority because the customer has already done their part.
The 116 tickets in "Waiting Customer" status deserve a second look too. If a ticket has been waiting for a customer response for over 31 days, it is likely abandoned. Closing or re-engaging on these tickets would reduce the backlog count and give a clearer picture of real workload.
5 priorities based on the findings above
These tickets have a customer waiting for your team to act. They are all overdue. Sort them by age and start with the oldest. Every day a customer waits after responding increases the risk of an escalation, a bad review, or a lost account. This is the highest-impact group to clear.
116 tickets are waiting on a customer response. If the customer has not replied in 60+ days, the ticket is functionally dead. Send a final follow-up email with a 48-hour auto-close warning. This alone could reduce your open ticket count by 10-15% and give your team a more honest view of real workload.
A fifth of your backlog has never been touched. Review these tickets by creation date. Some may be duplicates, already resolved through other tickets, or no longer relevant. The rest need assignment and realistic due dates. Leaving tickets in "New" status for months signals a dispatch bottleneck.
These tickets are being actively worked on, but their due dates are meaningless because they all expired months ago. Reset the due dates to realistic targets so the team has something to work toward. A due date in the past provides zero motivational or planning value.
The root cause here is that due dates are treated as a one-time field, not a living planning tool. Add a weekly check (automated or manual) that flags any open ticket where the due date has passed. Dispatch should either close it, reassign it, or update the due date. Over time, this brings the "tickets due this week" metric back to life as a real work queue.
Every open ticket in the system already has a due date in the past. No tickets have been updated with future due dates, so the "due today" and "due this week" buckets are empty. This typically happens when due dates are set automatically at ticket creation and never adjusted as work progresses.
Not necessarily. Due date and SLA are separate fields in Autotask. A ticket can be overdue on its due date but still within its SLA resolution window, or vice versa. This report focuses on the due date field specifically, which is typically used for internal planning and scheduling.
The AI calculates the number of days between each ticket's due date and today, then averages across all 844 open tickets. The "due_date_age_category" field in Proxuma Power BI groups these into buckets (7 days, 14 days, 31+ days), but the raw average gives a single number for the overall health of the backlog.
Not without review. Some of those tickets may represent ongoing projects, recurring issues, or work that is still relevant but was never properly tracked. Start with the "Waiting Customer" and "New" statuses, where stale tickets are most likely to be safe to close. For "In progress" and "Planned" tickets, have the assigned technician review before closing.
Yes. Connect Proxuma Power BI to your Autotask PSA, 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.
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