Which SLA targets are being missed, how often, and what patterns in your ticket data point to the underlying causes. Generated by AI via Proxuma Power BI MCP server.
Which SLA targets are being missed, how often, and what patterns in your ticket data point to the underlying causes. 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 delivery managers, operations leads, and MSP owners tracking service quality
How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews
Which SLA targets are being missed, how often, and what patterns in your ticket data point to the underlying causes. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"TotalBreaches", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[resolved_due_age_days] > 0),
"TotalTickets", COUNTROWS(BI_Autotask_Tickets),
"BreachRate", DIVIDE(
CALCULATE(COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[resolved_due_age_days] > 0),
COUNTROWS(BI_Autotask_Tickets)),
"OpenTickets", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] IN {
"In progress", "New", "Waiting Customer",
"Planned", "Customer has responded"})
)SLA performance across all ticket priorities, showing where first response and resolution targets are being missed
| Priority | Tickets | FR Met % | Res Met % | Avg Overdue Days | Severity |
|---|---|---|---|---|---|
| P1 - Critical | 1,788 | 68.6% | 71.8% | 82.1 | Critical |
| P2 - High | 5,019 | 82.4% | 94.0% | 55.7 | Watch |
| P3 - Medium | 14,715 | 55.2% | 83.8% | 69.3 | Critical |
| P4 - Low | 30,415 | 83.5% | 90.6% | 71.4 | Watch |
| Service/Change | 15,584 | 97.3% | 97.5% | 165.1 | Healthy |
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Tickets[priority_name],
"TicketCount", COUNTROWS(BI_Autotask_Tickets),
"FirstResponseMetPct", [Tickets - First Response Met %],
"ResolutionMetPct", [Tickets - Resolution Met %],
"AvgResolveDays", AVERAGE(BI_Autotask_Tickets[resolved_due_age_days])
)How each priority performs against first response targets. A missed first response means the customer waited longer than the SLA allows before getting any acknowledgment.
| Priority | Tickets | FR Missed | FR Miss Rate | Scale |
|---|---|---|---|---|
| P3 - Medium | 14,715 | 6,593 | 44.8% | |
| P4 - Low | 30,415 | 5,018 | 16.5% | |
| P2 - High | 5,019 | 883 | 17.6% | |
| P1 - Critical | 1,788 | 562 | 31.4% | |
| Service/Change | 15,584 | 421 | 2.7% |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[priority_name]
),
"TicketCount", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"FRMissed", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[first_response_met] + 0 = 0),
"FRMissRate", DIVIDE(
CALCULATE(COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[first_response_met] + 0 = 0),
CALCULATE(COUNTROWS(BI_Autotask_Tickets)))
)
ORDER BY [FRMissed] DESCWhere resolution deadlines are being missed. A resolution breach means the ticket stayed open past its SLA target, regardless of whether first response was met.
| Priority | FR Missed | FR Miss % | Res Missed | Res Miss % |
|---|---|---|---|---|
| P1 | 2,393 | 47.7% | 420 | 8.4% |
| P2 | 1,149 | 64.3% | 822 | 46.0% |
| P3 | 9,650 | 65.6% | 4,430 | 30.1% |
| P4 | 11,830 | 38.9% | 9,016 | 29.6% |
| Service/Change | 6,784 | 43.5% | 9,941 | 63.8% |
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[priority_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'), "FRNotMet", COUNTROWS('BI_Autotask_Tickets') - CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "ResNotMet", COUNTROWS('BI_Autotask_Tickets') - CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1))What the data tells us about why these breaches are happening
The breach data points to three distinct failure patterns, each with a different root cause.
Pattern 1: P3 first response is broken at scale. With 14,715 tickets and a 44.8% first response miss rate, P3 is not a marginal problem. Nearly half of all medium-priority tickets go unacknowledged within SLA. This usually happens when dispatch rules deprioritize P3 in favor of P1 and P2, or when auto-assignment is not configured for medium-priority queues. The volume alone (6,593 missed) means this is a systemic issue, not individual technician behavior.
Pattern 2: P1 resolution takes too long when it fails. Critical tickets that miss resolution SLA average 82.1 days overdue. A P1 ticket should resolve in hours or days, not months. An 82-day average suggests these are tickets that escalated beyond the team's ability to resolve, got stuck waiting on a vendor, or were deprioritized once the immediate fire was out. The 28.2% miss rate on 1,788 tickets means roughly 504 critical tickets sat well past their deadline.
Pattern 3: Service/Change tickets are forgotten. Only 2.5% of Service/Change tickets breach resolution SLA, but the average overdue is 165 days. These are likely project-type tickets or change requests that were opened, partially worked, and then abandoned. The SLA clock kept running because nobody closed or reclassified them. The low breach rate means automation is handling most of them well. The outliers need a manual review.
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Tickets[priority_name],
"TicketCount", COUNTROWS(BI_Autotask_Tickets),
"FirstResponseMetPct", [Tickets - First Response Met %],
"ResolutionMetPct", [Tickets - Resolution Met %],
"AvgResolveDays", AVERAGE(BI_Autotask_Tickets[resolved_due_age_days])
)Currently open tickets that could breach SLA if not addressed. These are the tickets most likely to appear in next month's breach numbers.
| Status | Ticket Count | Risk Level | Action |
|---|---|---|---|
| Waiting Customer | 218 | Medium | SLA clock paused, but stale tickets drag out resolution |
| Planned | 213 | Medium | Often parked indefinitely. Review for stale entries. |
| In Progress | 198 | High | SLA clock running. Check age against SLA target. |
| New | 169 | High | Not yet assigned. First response SLA at risk. |
| Customer has responded | 102 | High | Customer is waiting. SLA clock restarted. |
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Tickets[priority_name],
BI_Autotask_Tickets[status_name],
"TicketCount", COUNTROWS(BI_Autotask_Tickets)
)5 priorities based on the findings above
A 44.8% first response miss rate across 14,715 tickets is not a performance issue, it is a process gap. Check whether P3 tickets have auto-assignment rules in Autotask. If dispatch relies on manual triage, P3 tickets will always lose to P1 and P2 in the queue. Set up auto-routing for P3 tickets to a dedicated queue or technician group. The absolute volume of 6,593 missed first responses means this one fix will have the biggest impact on your overall SLA numbers.
The average overdue of 82.1 days on P1 resolution failures means some critical tickets are sitting for months past deadline. Pull the list of P1 tickets with resolved_due_age_days > 30 and review each one. Common culprits: vendor escalations without follow-up, tickets stuck in a “waiting on parts” status that nobody checks, or tickets that were resolved informally but never closed in Autotask. 504 critical tickets missed resolution SLA. Even closing the stale ones will improve your numbers and your reporting accuracy.
Every ticket in “New” status is burning through its first response SLA right now. Sort them by creation date and priority, assign the oldest ones first. If any P1 or P2 tickets are sitting in “New,” those are already breached or about to be. Set up an Autotask alert that fires when any ticket stays in “New” for more than 30 minutes.
Service and Change tickets that breach sit overdue for an average of 165 days. These are forgotten projects and abandoned change requests. Run a report on all Service/Change tickets older than 90 days. Either complete them, cancel them, or reclassify them. Leaving them open inflates your backlog and gives a misleading picture of workload.
102 tickets where the customer has replied and is waiting for your team. The SLA clock restarted when they responded. Set a workflow rule in Autotask that bumps “Customer has responded” tickets to the top of the queue. A customer who took the time to reply and gets silence back is a churn risk. This is an easy win that improves both SLA compliance and client perception.
A ticket is counted as breached when its resolved_due_age_days value is greater than zero. This field in Autotask tracks how many days past the resolution deadline the ticket was when it was finally resolved. Separately, the first_response_met and resolution_met fields (integer flags) indicate whether each SLA target was hit. This report uses all three fields to give a complete picture.
The 0.53% figure counts only tickets where the resolution went past the SLA deadline. First response misses are tracked separately. When you look at first response compliance, the picture changes: P3 tickets miss first response 44.8% of the time, and P1 tickets miss it 31.4% of the time. The low resolution breach rate is partly because most tickets eventually get resolved, just not always within SLA.
This demo report covers the full dataset period rather than a single month, because the synthetic data does not include per-month filtering. When you run this against your own data, you can add a date filter on BI_Autotask_Tickets[create_date] or use Power BI's date table to scope it to any time range you need.
FR Met % is the percentage of tickets where the first technician response happened within the SLA window. Res Met % is the percentage where the ticket was fully resolved within the SLA window. These are separate SLA targets. A ticket can meet first response but miss resolution, or vice versa. Both are measured from Autotask's first_response_met and resolution_met fields.
Service and Change request tickets that breach tend to be project-type work or long-running change requests. The SLA breach rate is low (2.5%), meaning most get handled fine. But the small number that do breach are tickets that were opened, partially worked, and then stalled. Nobody closed them or extended the deadline, so the overdue counter kept climbing. A quarterly cleanup of stale Service/Change tickets prevents this.
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. You can filter by date, client, board, or any other dimension in the Autotask data model.
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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