How close your first response and resolution rates get to industry-standard SLA targets, broken down by priority. Generated by AI via Proxuma Power BI MCP server.
How close your first response and resolution rates get to industry-standard SLA targets, broken down by priority. 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 close your first response and resolution rates get to industry-standard SLA targets, broken down by priority. Generated by AI via Proxuma Power BI MCP server.
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])
)Industry-standard SLA targets compared against actual first response and resolution performance, per priority level. Negative gaps mean you are below target.
| Priority | Tickets | FR % | Res % |
|---|---|---|---|
| P1 - Kritisch | 5,019 | 52.3% | 91.6% |
| P2 - Hoog | 1,788 | 35.7% | 54.0% |
| P3 - Medium | 14,715 | 34.4% | 69.9% |
| P4 - Laag | 30,415 | 61.1% | 70.4% |
| Service/Change | 15,584 | 56.5% | 36.2% |
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[priority_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'), "AvgFirstResponseHours", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "FirstResponseMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "AvgResolutionHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]), "ResolutionMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1))How quickly each priority level gets its first response compared to what the SLA requires
| Priority | FR Window | Target | Actual | Gap | Status |
|---|---|---|---|---|---|
| P1 — Critical | Within 15 min | 95.0% | 68.6% | -26.4pp | Critical Miss |
| P2 — High | Within 30 min | 90.0% | 82.4% | -7.6pp | Below Target |
| P3 — Medium | Within 2 hrs | 85.0% | 55.2% | -29.8pp | Critical Miss |
| P4 — Low | Within 4 hrs | 80.0% | 83.5% | +3.5pp | Exceeds |
First response is the weakest metric across the board. Only P4 tickets exceed their target. P3 is the most alarming: with 14,715 tickets and only 55.2% meeting the 2-hour first response window, nearly half of all medium-priority tickets go without a first response within the SLA. P1 tickets also miss badly at 68.6% against a 95% target. Both of these point to a triage and dispatch problem, not a resolution capacity issue.
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Tickets[priority_name],
"TicketCount", COUNTROWS(BI_Autotask_Tickets),
"FirstResponseMetPct", [Tickets - First Response Met %]
)Whether tickets get resolved within their SLA window, per priority level
| Priority | Resolution Window | Target | Actual | Gap | Avg Days | Status |
|---|---|---|---|---|---|---|
| P1 — Critical | Within 4 hrs | 95.0% | 71.8% | -23.2pp | 82.1 | Critical Miss |
| P2 — High | Within 8 hrs | 90.0% | 94.0% | +4.0pp | 55.7 | Exceeds |
| P3 — Medium | Within 24 hrs | 85.0% | 83.8% | -1.2pp | 69.3 | Near Target |
| P4 — Low | Within 72 hrs | 80.0% | 90.6% | +10.6pp | 71.4 | Exceeds |
| Service/Change | Per agreement | N/A | 97.5% | N/A | 165.1 | Strong |
Resolution tells a better story than first response. P2 exceeds target by 4.0 percentage points and P4 by 10.6pp. Once tickets are picked up, the team gets them closed within SLA. P3 is close at 83.8% against an 85% target. The only real problem is P1: critical tickets resolve at 71.8% against a 95% target, with an average resolution time of 82.1 days. That average includes tickets that sat open for months, dragging the number up. It is worth investigating whether those are true P1s or misclassified tickets.
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Tickets[priority_name],
"TicketCount", COUNTROWS(BI_Autotask_Tickets),
"ResolutionMetPct", [Tickets - Resolution Met %],
"AvgResolveDays", AVERAGE(BI_Autotask_Tickets[resolved_due_age_days])
)The two priority levels with the largest SLA gaps and what the numbers suggest
Out of 1,788 P1 tickets, only 68.6% received a first response within the 15-minute SLA window. That means 562 critical tickets waited too long for initial contact. Resolution is similarly off: 71.8% against a 95% target leaves 504 P1 tickets unresolved within SLA. The 82.1-day average resolution time suggests a subset of these tickets stayed open far too long, possibly due to misclassification or dependency on external vendors. This is the highest-risk gap in the dataset.
P3 is the largest gap by volume. With 14,715 tickets, a 55.2% first response rate means 6,592 tickets did not get a first response within 2 hours. Resolution is much closer at 83.8% (1.2pp off target), which means the problem is specifically about initial pickup speed, not about the ability to solve the issue. This pattern usually points to dispatch queue configuration, auto-assignment rules, or technician availability during peak hours.
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Tickets[priority_name],
FILTER(
VALUES(BI_Autotask_Tickets[priority_name]),
BI_Autotask_Tickets[priority_name] IN {"P1 - Critical", "P3 - Medium"}
),
"TicketCount", COUNTROWS(BI_Autotask_Tickets),
"FirstResponseMetPct", [Tickets - First Response Met %],
"ResolutionMetPct", [Tickets - Resolution Met %],
"AvgResolveDays", AVERAGE(BI_Autotask_Tickets[resolved_due_age_days])
)Priority levels that meet or beat their SLA targets, and what that tells you
P4 is the largest bucket at 30,415 tickets. First response lands at 83.5% (target: 80%) and resolution at 90.6% (target: 80%). The 4-hour first response window and 72-hour resolution window give the team enough room to work. This is also where well-configured auto-responses and ticket routing pay off. If the same routing logic were applied to P3, you would likely close the first response gap.
P2 resolution performance is strong. The first response gap of 7.6pp is the smallest miss in the dataset and could be closed with minor operational changes. At 5,019 tickets, P2 accounts for the second-largest priority tier after P4 and P3. The team resolves these tickets well once they pick them up.
Service and change requests run on a different workflow and typically have longer built-in SLA windows. The 97.5% resolution rate is the highest in the dataset. The 165.1-day average resolution time reflects the nature of these tickets: planned changes, projects, and procurement that take weeks or months by design, not by failure.
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Tickets[priority_name],
FILTER(
VALUES(BI_Autotask_Tickets[priority_name]),
BI_Autotask_Tickets[priority_name] IN {
"P2 - High", "P4 - Low", "Service/Change Request"
}
),
"TicketCount", COUNTROWS(BI_Autotask_Tickets),
"FirstResponseMetPct", [Tickets - First Response Met %],
"ResolutionMetPct", [Tickets - Resolution Met %]
)5 priorities based on the gap analysis above
P3 has the largest gap in the report at 29.8 percentage points below target. With 14,715 tickets in this tier, it represents the bulk of your SLA misses. Check your dispatch queue rules for P3 tickets. Are they auto-assigned? Do they sit in a general queue waiting for manual pickup? The resolution rate at 83.8% shows the team can handle them once picked up. The bottleneck is the initial response, not the skill to resolve.
A 68.6% first response rate on critical tickets is a contract risk. Start by auditing whether all 1,788 P1 tickets were genuinely critical. MSPs frequently see P1 inflation from end users or automated alerts that should be P2 or P3. Then check whether your on-call process guarantees a response within 15 minutes. The 82.1-day average resolution suggests some P1s lingered for months. Pull the outliers and reclassify or close them.
P2 resolution already exceeds target at 94.0%. The first response shortfall of 7.6pp is the smallest gap to close. Consider implementing a dedicated P2 alert or a shorter auto-assign timeout. At 5,019 tickets, even a modest improvement in first response would move the needle on overall SLA compliance and client perception.
A 95% target on P1 first response within 15 minutes is aggressive. If your team consistently lands at 68.6%, the target may not be achievable with your current staffing model. That does not mean you lower expectations. It means you either invest in the staffing and tooling to hit 95%, or you set an honest interim target (e.g., 80%) and build a roadmap to get there. Promising 95% and delivering 68.6% is worse than promising 80% and delivering 83%.
P4 exceeds both targets with 30,415 tickets. Service/Change hits 97.5% resolution across 15,584 tickets. These are not accidents. Your ticket routing, auto-assignment, and SLA windows for these tiers are set correctly. Use the same operational patterns as a template when fixing the P1 and P3 gaps. Show clients that when the process is configured correctly, you deliver.
The targets used in this report are industry-standard MSP SLA benchmarks: 95% for P1, 90% for P2, 85% for P3, and 80% for P4. Your own SLA agreements may differ. You can adjust the targets in the comparison table to match your specific contracts when running this against your own data.
Percentage points measure the absolute difference between two percentages. If your target is 95% and your actual is 68.6%, the gap is -26.4 percentage points (pp). This is different from saying "26.4% below target," which would be a relative comparison. Percentage points give a clearer picture of the actual shortfall.
Average resolution time includes all tickets, even those that stayed open for months. A small number of P1 tickets with extended resolution times (waiting on vendor, misclassified, or left open accidentally) can pull the average up significantly. Median resolution time would give a more representative picture. Auditing the outliers is the first step.
Service and change requests typically have custom SLA windows defined per agreement rather than a universal industry standard. Their SLA terms vary by the type of change (standard, normal, emergency) and the client contract. The performance data is still shown because 97.5% resolution is a strong data point worth highlighting.
Yes. The DAX queries in this report work against the full dataset, but you can add filters for company name, date range, or ticket queue. For QBR preparation, filter to the client and the last quarter. The same gap analysis applies at any level of detail.
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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