Which service queues hit their SLA targets and which ones consistently fall short. Ranked across 16 queues and 67,521 tickets. Generated by AI via Proxuma Power BI MCP server.
Which service queues hit their SLA targets and which ones consistently fall short. Ranked across 16 queues and 67,521 tickets. 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 service queues hit their SLA targets and which ones consistently fall short. Ranked across 16 queues and 67,521 tickets. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"TotalTickets", COUNTROWS(BI_Autotask_Tickets),
"FR_Met", COUNTROWS(FILTER(BI_Autotask_Tickets, [first_response_met] + 0 = 1)),
"FR_Pct", DIVIDE(
COUNTROWS(FILTER(BI_Autotask_Tickets, [first_response_met] + 0 = 1)),
COUNTROWS(BI_Autotask_Tickets)),
"Res_Met", COUNTROWS(FILTER(BI_Autotask_Tickets, [resolution_met] + 0 = 1)),
"Res_Pct", DIVIDE(
COUNTROWS(FILTER(BI_Autotask_Tickets, [resolution_met] + 0 = 1)),
COUNTROWS(BI_Autotask_Tickets)),
"Queues", DISTINCTCOUNT(BI_Autotask_Tickets[queue_name])
)All 16 queues ranked by resolution SLA compliance, with first response rate, ticket volume, and average worked hours
| Queue | Tickets | FR Met | FR % | Res Met | Res % |
|---|---|---|---|---|---|
| L1 Support | 31,378 | 19,949 | 63.6% | 18,585 | 59.2% |
| Centralized Services | 17,082 | 5,816 | 34.0% | 12,783 | 74.8% |
| L2 Support | 7,889 | 4,234 | 53.7% | 5,748 | 72.9% |
| Merged Tickets | 4,999 | 2,878 | 57.6% | 3,281 | 65.6% |
| Technical Alignment | 2,316 | 1,005 | 43.4% | 913 | 39.4% |
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[queue_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'), "SLAFirstResponseMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "SLAResolutionMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1))The three highest and three lowest queues by resolution SLA, with visual comparison
| Queue | Tickets | FR % | Res % | Resolution SLA |
|---|---|---|---|---|
| Recurring (Parked) | 98 | 94.9% | 91.8% | |
| Monitoring | 17,082 | 34.0% | 74.8% | |
| L2 Support | 7,889 | 53.7% | 72.9% |
| Queue | Tickets | FR % | Res % | Resolution SLA |
|---|---|---|---|---|
| Compliancy | 29 | 13.8% | 10.3% | |
| Sales | 107 | 38.3% | 23.4% | |
| Consultancy | 546 | 53.1% | 31.3% |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets, BI_Autotask_Tickets[queue_name]),
"Tickets", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"Avg_Hours", CALCULATE(AVERAGE(BI_Autotask_Tickets[worked_hours])),
"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 [Tickets] DESCQueues with 700+ tickets where SLA compliance is below 60% on either metric. These represent the biggest operational risk because volume amplifies every percentage point of failure.
| Queue | Tickets | Avg Hrs | FR % | Res % | Risk |
|---|---|---|---|---|---|
| Servicedesk | 31,378 | 0.57 | 63.6% | 59.2% | Resolution below 60% |
| Monitoring | 17,082 | 0.83 | 34.0% | 74.8% | FR severely low |
| L2 Support | 7,889 | 1.28 | 53.7% | 72.9% | FR below 60% |
| Projects | 2,316 | 3.03 | 43.4% | 39.4% | Both below 50% |
| Customer succes | 804 | 1.47 | 43.5% | 35.1% | Both below 50% |
| Interne IT | 793 | 0.42 | 25.6% | 39.8% | Both below 40% |
| Onsite support | 705 | 2.40 | 67.2% | 45.7% | Resolution below 50% |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets, BI_Autotask_Tickets[queue_name]),
"Tickets", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"Avg_Hours", CALCULATE(AVERAGE(BI_Autotask_Tickets[worked_hours])),
"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 [Tickets] DESCThe difference between first response and resolution SLA rates reveals where tickets get acknowledged quickly but resolved slowly, or vice versa
| Queue | FR % | Res % | Gap | Pattern |
|---|---|---|---|---|
| Monitoring | 34.0% | 74.8% | +40.8 pp | Slow pickup, fast resolution |
| Onsite support | 67.2% | 45.7% | -21.5 pp | Fast pickup, slow resolution |
| Consultancy | 53.1% | 31.3% | -21.8 pp | Fast pickup, slow resolution |
| L2 Support | 53.7% | 72.9% | +19.2 pp | Slow pickup, fast resolution |
| Sales | 38.3% | 23.4% | -14.9 pp | Both weak |
| Interne IT | 25.6% | 39.8% | +14.2 pp | Slow pickup, slow resolution |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets, BI_Autotask_Tickets[queue_name]),
"Tickets", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"Avg_Hours", CALCULATE(AVERAGE(BI_Autotask_Tickets[worked_hours])),
"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 [Tickets] DESCThe global numbers tell a familiar story: 52.9% first response compliance and 63.5% resolution compliance across 67,521 tickets. Those averages are fine for a board slide but useless for fixing anything. The variation between queues is where the real picture emerges.
Recurring (Parked) is the top performer at 94.9% first response and 91.8% resolution, but with only 98 tickets this is more of a housekeeping queue than a service delivery benchmark. The real leaders are Monitoring (74.8% resolution on 17,082 tickets) and L2 Support (72.9% resolution on 7,889 tickets). Both handle serious volume and still deliver above the global average.
The Monitoring queue has an interesting pattern: its first response rate is just 34.0% while resolution hits 74.8%. That 40.8 percentage point gap suggests automated ticket creation (monitoring alerts) floods the queue faster than technicians can acknowledge, but once someone picks up the ticket, they resolve it quickly. If your SLA clock starts at ticket creation for monitoring alerts, consider whether that SLA target is realistic for auto-generated tickets.
Interne IT is the worst high-volume queue. With 793 tickets, a 25.6% first response rate, and a 39.8% resolution rate, this queue fails on both counts. The average worked hours of 0.42 suggests these are quick tasks that sit waiting in a queue nobody prioritizes. Internal IT tickets may lack the urgency of client-facing work, but a 25.6% first response rate signals a structural neglect problem.
Projects (2,316 tickets) at 39.4% resolution is the largest queue below 40%. The 3.03 average worked hours confirms these are complex items, but the 43.4% first response rate means tickets are not even being acknowledged in time. This queue likely needs dedicated project coordinators with clear SLA ownership, not the same dispatch rules as break-fix tickets.
Compliancy has the lowest numbers across the board at 13.8% FR and 10.3% resolution, but with only 29 tickets the sample is small. Still, 10.3% resolution compliance means 26 out of 29 tickets missed their target. Worth checking whether the SLA targets for this queue are configured correctly in Autotask.
5 priorities based on the findings above
A 25.6% first response rate on 793 tickets means three out of four internal tickets are ignored past the SLA deadline. Assign a specific team member or rotation to own this queue. Internal IT tickets often get deprioritized because they do not generate client complaints, but they still represent real work that real colleagues need done. The 0.42 average hours shows these are fast to resolve once someone actually starts.
The 34.0% first response rate on 17,082 tickets is the single largest SLA gap by volume. If monitoring alerts auto-create tickets, your first response SLA may be unrealistic for that queue. Either adjust the SLA target for auto-generated tickets, set up auto-acknowledgment rules, or route low-priority alerts to a separate queue with a different SLA. Fixing this alone could lift your global FR% by several points.
Project tickets (2,316 total, 3.03 avg hours, 39.4% resolution) should not share the same SLA framework as break-fix. Projects are inherently longer-running. Set up a separate SLA policy in Autotask for the Projects queue with targets that reflect project timelines, not incident response. This removes noise from your SLA reporting and lets you track project delivery on its own terms.
Your highest-volume queue (31,378 tickets) hits 63.6% on first response but drops to 59.2% on resolution. That gap means the Servicedesk picks up tickets on time but cannot close them fast enough. Look for patterns: are tickets being escalated out of the queue and losing SLA? Are complex tickets sitting in the Servicedesk queue instead of being routed to L2? A 4-point improvement in Servicedesk resolution alone would move the global number.
Monitoring at 74.8% resolution on 17,082 tickets and L2 Support at 72.9% on 7,889 tickets prove that high volume does not have to mean low compliance. Study what these queues do differently: dispatch rules, staffing levels, ticket categorization. Apply those patterns to the underperforming queues. If Monitoring can resolve at 74.8% on 17K tickets, the Servicedesk should be able to beat 59.2% on 31K.
A ticket counts as first response met when a technician posts an update or changes the ticket status before the SLA-defined first response deadline. This is tracked by the first_response_met field in Autotask. The Proxuma Power BI model treats this as a boolean flag (1 = met, 0 = breached) and the DAX query filters on [first_response_met] + 0 = 1 to handle the int64 data type.
Monitoring tickets are typically auto-created by RMM alerts. The SLA timer starts at ticket creation, which means the clock is already running before a human even sees the ticket. If your monitoring tool generates hundreds of alerts during off-hours, many will breach the first response SLA by the time the team starts their shift. The resolution rate (74.8%) is much higher because once a technician picks up the alert, the fix is usually straightforward.
Yes. A one-size-fits-all SLA across queues like Servicedesk (0.57 avg hours) and Consultancy (3.88 avg hours) produces misleading numbers. Autotask allows you to define SLA policies per queue. Set aggressive targets for break-fix queues (Servicedesk, L2) and more generous ones for project-based or consultancy work. This gives you honest compliance rates that reflect actual performance.
Focus on the highest-volume queues first. Servicedesk (31,378 tickets) and Monitoring (17,082 tickets) together represent 72% of all tickets. A 5-point improvement in Servicedesk resolution alone would add roughly 1,500 more compliant tickets. For Monitoring, auto-acknowledge rules for RMM-generated tickets could lift the first response rate significantly without adding staff.
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. Your queue names and SLA targets will be different, but the analysis structure stays the same.
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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