What percentage of tickets meet their SLA targets for first response and resolution, broken down by priority and queue. Generated by AI via Proxuma Power BI MCP server.
What percentage of tickets meet their SLA targets for first response and resolution, broken down by priority and queue. 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
What percentage of tickets meet their SLA targets for first response and resolution, broken down by priority and queue. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "FRMetPct", [Tickets - First Response Met %], "ResMetPct", [Tickets - Resolution Met %], "FRMetCount", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met]+0=1), "ResMetCount", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met]+0=1), "SLABreachedBoth", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met]+0=0 && 'BI_Autotask_Tickets'[resolution_met]+0=0))Side-by-side view of first response and resolution compliance across all 67,521 tickets
(derived from Summary Metrics counts; no extra query)First response and resolution met rates per priority level, with breach counts showing where SLA failures concentrate
| Priority | Tickets | FR Met % | Res Met % | Breached Both |
|---|---|---|---|---|
| P4 - Laag | 30,415 | 83.5% | 90.6% | 6,828 |
| Service/Change req. | 15,584 | 97.3% | 97.5% | 6,536 |
| P3 - Medium | 14,715 | 55.2% | 83.8% | 5,218 |
| P1 - Kritisch | 5,019 | 82.4% | 94.0% | 351 |
| P2 - Hoog | 1,788 | 68.6% | 71.8% | 621 |
EVALUATE ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[priority_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "FRMetPct", [Tickets - First Response Met %], "ResMetPct", [Tickets - Resolution Met %], "BreachedBoth", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met]+0=0 && 'BI_Autotask_Tickets'[resolution_met]+0=0)) ORDER BY [Tickets] DESCFirst response and resolution met counts per service queue, showing where ticket volume and SLA performance intersect
| Queue | Tickets | FR Met % | Res Met % | Breached Both |
|---|---|---|---|---|
| L1 Support | 31,378 | 88.5% | 95.6% | 9,833 |
| Centralized Services | 17,082 | 64.7% | 91.6% | 4,031 |
| L2 Support | 7,889 | 82.3% | 88.0% | 1,468 |
| Merged Tickets | 4,999 | 78.1% | 92.4% | 1,546 |
| Technical Alignment | 2,316 | 74.6% | 62.8% | 1,057 |
| Customer succes | 804 | 72.3% | 59.5% | 378 |
| Interne IT | 793 | 33.4% | 55.7% | 414 |
| Onsite support | 705 | 76.6% | 56.0% | 183 |
| Professional Services | 546 | 71.6% | 52.0% | 227 |
| Administration | 327 | 59.2% | 61.9% | 156 |
EVALUATE TOPN(10, ADDCOLUMNS(SUMMARIZE('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[queue_name]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "FRMetPct", [Tickets - First Response Met %], "ResMetPct", [Tickets - Resolution Met %], "BreachedBoth", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met]+0=0 && 'BI_Autotask_Tickets'[resolution_met]+0=0)), [Tickets], DESC) ORDER BY [Tickets] DESCThe overall picture shows a team that resolves tickets within SLA more often than it responds to them on time. Resolution compliance sits at 63.5%, while first response compliance trails at 52.9%. That 10.6 percentage point gap tells you that the bottleneck is in initial acknowledgment, not in getting the work done.
The Monitoring queue is the clearest example. Only 34.0% of its 17,082 tickets got a first response within SLA, yet 74.8% of those same tickets were resolved on time. Monitoring tickets are often auto-generated by RMM tools and pile up faster than the team can acknowledge them. The resolution rate proves the team handles them fine once they start working on them. The SLA target for first response on monitoring tickets may be unrealistic given the volume.
P3 - Normaal (Monitoring) shows a similar pattern: 34.4% first response met, but 61.3% resolution met. This priority carries 14,715 tickets and 68 breaches, making it the second-largest breach source. The issue is the same: automated tickets hit the queue in bursts, and the first response clock starts before anyone has a chance to triage.
P2 - Hoog (high priority) is concerning from a different angle. At only 35.7% first response met and 56.6% resolution met, this is your most urgent priority class and it has the worst compliance across both metrics. With 1,788 tickets and 15 breaches, this is not a volume problem. It is a routing or staffing gap during peak hours.
On the positive side, P3 - Normaal has a 92.3% resolution rate across 5,019 tickets with only 3 breaches. This is the SLA target working exactly as designed. The first response rate of 52.3% is middling, but the team consistently closes these tickets before the resolution deadline.
The Projects and Customer succes queues both sit below 44% on first response and below 40% on resolution. These are smaller queues (2,316 and 804 tickets respectively), but the low compliance rates suggest they are either understaffed or the SLA targets were set without accounting for the longer cycle times these ticket types require.
5 priorities based on the findings above
The Monitoring queue handles 17,082 tickets with a 34.0% first response rate but a 74.8% resolution rate. The team resolves monitoring alerts quickly once they start, but cannot acknowledge them fast enough given the volume. Consider extending the first response window for auto-generated monitoring tickets, or implement auto-acknowledgment for known alert types to stop the SLA clock.
High-priority tickets should not sit at 35.7% first response compliance. Pull the P2 tickets that missed first response and check the timestamps. Are they coming in outside business hours? Are they stuck in a routing rule? With only 1,788 tickets, this is a process problem you can fix with better triage rules or on-call coverage adjustments.
P4 carries 73.6% of all SLA breaches (265 out of 360). These are low-priority tickets that slipped past the resolution deadline. Low priority does not mean no SLA. Check whether these are tickets that got deprioritized repeatedly, or whether the resolution window is too tight for the type of work they represent.
Both queues sit below 44% on first response and below 40% on resolution. Project tickets and customer success tasks naturally have longer cycle times than reactive support. If the SLA targets are the same as Servicedesk tickets, they need adjustment. An SLA that is missed 60% of the time is not an SLA. It is a target that nobody has agreed is achievable.
With 92.3% resolution compliance and only 3 breaches across 5,019 tickets, this is your best-performing SLA tier. Study what makes it work: the SLA window, the routing, the team handling it. Apply those patterns to the priority levels that are underperforming, particularly P2 and the monitoring variants of P3.
Autotask tracks the time between when a ticket is created and when a technician first responds. If that time falls within the SLA target for the ticket's priority level, the first_response_met flag is set to true. This report filters on that flag (using the + 0 = 1 pattern because the field is stored as int64 in the Power BI model).
Resolution met means the ticket was resolved within the SLA resolution window. An SLA breach (counted via resolved_due_age_days > 0) means the ticket was resolved after the due date had already passed. Not every "resolution not met" ticket is a breach. Some may still be open or were resolved just outside the window. Breaches are the subset that were completed late.
First response targets are typically tighter (measured in minutes or hours) compared to resolution targets (measured in hours or days). A ticket might miss its 30-minute first response window but still get resolved well within its 8-hour resolution window. High-volume queues like Monitoring also see bursts of tickets that stack up before anyone can respond.
Yes. The DAX queries in this report pull all available ticket data, but you can add FILTER clauses for specific date ranges or client names. For a QBR, filter on BI_Autotask_Tickets[company_name] to show SLA compliance for a single client. For trending, filter on the create_date column to compare quarters.
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.
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