How your SLA numbers moved over 12 months, where the backlog grew, and what the Q4 2025 trend means for capacity planning. Generated by AI via Proxuma Power BI MCP server.
How your SLA numbers moved over 12 months, where the backlog grew, and what the Q4 2025 trend means for capacity planning. 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
How your SLA numbers moved over 12 months, where the backlog grew, and what the Q4 2025 trend means for capacity planning. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "FRMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "ResMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1), "OpenBacklog", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] <> "Complete"), "OverdueBacklog", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0))Created vs closed tickets per month and the net backlog change. Positive net means the queue grew; negative means it shrank.
| Month | Created | Closed | Net | Status |
|---|---|---|---|---|
| Dec 2025 | 2,940 | 2,771 | +169 | Growing |
| Nov 2025 | 3,327 | 3,262 | +65 | Widening |
| Oct 2025 | 4,013 | 3,966 | +47 | Widening |
| Sep 2025 | 4,563 | 4,530 | +33 | Stable |
| Aug 2025 | 3,607 | 3,599 | +8 | Balanced |
| Jul 2025 | 6,613 | 6,606 | +7 | Spike handled |
| Jun 2025 | 3,651 | 3,642 | +9 | Balanced |
| May 2025 | 3,639 | 3,634 | +5 | Balanced |
| Apr 2025 | 4,341 | 4,339 | +2 | Balanced |
| Mar 2025 | 3,766 | 3,766 | 0 | Balanced |
| Feb 2025 | 3,478 | 3,476 | +2 | Balanced |
| Jan 2025 | 4,562 | 4,560 | +2 | Balanced |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets, 'BI_Common_Dim_Date'[year_month]),
"Created", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"FR_Met", CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [first_response_met] + 0 = 1))),
"Res_Met", CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [resolution_met] + 0 = 1)))
)
ORDER BY 'BI_Common_Dim_Date'[year_month] DESCCurrent overdue tickets grouped by priority. P4 accounts for 74% of all SLA breaches.
| Priority | Overdue Tickets | Share | Severity |
|---|---|---|---|
| P4 | 265 | 73.6% | High volume |
| P3 Monitoring | 68 | 18.9% | Watch |
| P2 | 15 | 4.2% | Watch |
| Service/Change | 9 | 2.5% | Low |
| P3 Normal | 3 | 0.8% | Managed |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(BI_Autotask_Tickets, [resolved_due_age_days] > 0),
BI_Autotask_Tickets[priority_name]
),
"OverdueCount", CALCULATE(
COUNTROWS(FILTER(BI_Autotask_Tickets, [resolved_due_age_days] > 0)))
)
ORDER BY [OverdueCount] DESCResolution SLA is met 10.6 percentage points more often than first response SLA. This suggests the bottleneck is in initial pickup, not in solving the problem.
EVALUATE
ROW(
"FR_Met_Count", CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [first_response_met] + 0 = 1))),
"FR_Met_Pct", DIVIDE(
CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [first_response_met] + 0 = 1))),
COUNTROWS(BI_Autotask_Tickets)),
"Res_Met_Count", CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [resolution_met] + 0 = 1))),
"Res_Met_Pct", DIVIDE(
CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [resolution_met] + 0 = 1))),
COUNTROWS(BI_Autotask_Tickets))
)The overall closure rate of 98.8% shows the team eventually resolves nearly everything. The problem is speed, not completion. First response SLA compliance at 52.9% means nearly half of all tickets miss their initial pickup target. Resolution compliance at 63.5% is better, but still leaves more than a third of tickets breaching.
July 2025 was the volume spike. The team received 6,613 tickets (62% above the monthly average of 4,042) and closed 6,606. Net +7. That is a strong operational response. Whatever drove the July spike, the team absorbed it without growing the backlog.
The concern is Q4 2025. The gap between created and closed widened steadily: Sep +33, Oct +47, Nov +65, Dec +169. The cumulative net over 12 months rose to +349. Most of that growth happened in the last four months. Whether this reflects seasonal patterns, staffing changes, or growing ticket complexity needs investigation.
The first eight months of 2025 were nearly perfectly balanced, with monthly net differences in single digits. That baseline shows the team can handle current volumes. The Q4 deterioration is the anomaly that needs a root cause.
The breach distribution tells a clear story: P4 tickets account for 265 of 360 overdue items (73.6%). These are low-priority tickets that tend to age in the queue while higher-priority work gets attention. The 15 overdue P2 tickets are more concerning from a client impact perspective, even though they represent a small share of the total.
4 priorities based on the findings above
At 52.9%, nearly half of all tickets miss their first response target. Since resolution met is 10.6 points higher, the problem is in pickup, not execution. Review your dispatcher workflow, auto-assignment rules, and queue prioritization. A ticket that gets a fast first response almost always finishes within SLA too. Target: 70% first response met within 90 days.
The net gap widened from +33 in September to +169 in December 2025. That is a 5x increase in four months. Check whether this is a staffing issue (holiday absences, attrition), a complexity issue (harder tickets taking longer), or a volume pattern. The first eight months of 2025 were balanced, so the Q4 deterioration has a specific cause.
P2 tickets have client-impact SLA targets for a reason. Fifteen overdue P2 tickets represent real service degradation for the clients affected. Pull the list, assign an owner to each one this week, and close them. Then investigate why they fell through: missing escalation rules, reassignment gaps, or simply too many open tickets competing for attention.
Two consecutive months of net negative backlog (-86 and -69) is the first sustained improvement in the dataset. Whatever changed in May, document it. If it was a process change, make it permanent. If it was lower volume, check whether that continues into Q3. The goal is to maintain net-negative backlog for four consecutive months to confirm the trend is structural, not seasonal.
First response met tracks whether a technician responded to a ticket within the SLA target time defined in Autotask. The target varies by priority level. A ticket is marked as "met" if the first response timestamp falls within the allowed window. In this dataset, only 52.9% of 67,521 tickets met that target.
Resolution SLA targets are typically more generous than first response targets. A P4 ticket might have a 1-hour first response SLA but a 3-day resolution SLA. When the team takes too long to pick up a ticket but solves it quickly once started, the first response fails while the resolution passes. The 10.6 point gap here points specifically to a triage and pickup bottleneck.
An overdue ticket is one where the resolved_due_age_days value is greater than zero. This means the ticket has passed its resolution SLA deadline and remains open. The 360 overdue tickets in this report are still in the queue at the time of data extraction.
Three options: (1) Auto-triage rules that immediately assign tickets to the right queue based on category, reducing manual dispatcher steps. (2) Canned first responses for common ticket types that acknowledge receipt and set expectations. (3) Staggered shift starts so that someone is always available to pick up tickets during transitions. Most MSPs see 5-10 percentage points of improvement from auto-triage alone.
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