Your average first response time is 6.3 hours across all priorities, but that number hides a problem. P2 - Hoog tickets wait 9.6 hours on average for a first response, with only 35.7% meeting the SLA target. This report breaks down response speed by priority, queue, ticket type, and monthly trend.
Your average first response time is 6.3 hours across all priorities, but that number hides a problem. P2 - Hoog tickets wait 9.6 hours on average for a first response, with only 35.7% meeting the SLA target. This report breaks down response speed by priority, queue, ticket type, and monthly trend.
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
Your average first response time is 6.3 hours across all priorities, but that number hides a problem. P2 - Hoog tickets wait 9.6 hours on average for a first response, with only 35.7% meeting the SLA target. This report breaks down response speed by priority, queue, ticket type, and monthly trend.
EVALUATE
ROW(
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"OverallAvgFirstResponseHours", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]),
"TotalFirstResponseMet", SUM('BI_Autotask_Tickets'[first_response_met]),
"AvgResolutionHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours])
)How quickly tickets get their first response, broken down by Autotask priority classification.
| Priority | Tickets | Avg FR (hrs) | FR Met | FR % |
|---|---|---|---|---|
| P1 - Kritisch | 5,019 | 0.83 | 2,626 | 52.3% |
| P2 - Hoog | 1,788 | 9.59 | 639 | 35.7% |
| P3 - Medium | 14,715 | 8.87 | 5,065 | 34.4% |
| P4 - Laag | 30,415 | 5.33 | 18,585 | 61.1% |
| Service/Change | 15,584 | 7.74 | 8,800 | 56.5% |
P1 tickets get the fastest response at 0.8 hours, which makes sense for critical issues. The surprising result is P2 (Hoog): despite being the second-highest priority, these tickets wait 9.6 hours on average and have the second-worst SLA compliance at 35.7%. P3 tickets actually fare worse on SLA compliance (34.4%) but P2's combination of high priority and slow response is the bigger operational risk.
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))Percentage of tickets where the first response was delivered within the SLA deadline.
Comparing first response performance across incident categories.
| Ticket Type | Tickets | Avg First Response | SLA Met | SLA % |
|---|---|---|---|---|
| Incident | 27,664 | 7.8h | 15,198 | 54.9% |
| Alert | 19,790 | 1.0h | 8,981 | 45.4% |
| Service Request | 12,653 | 9.7h | 6,657 | 52.6% |
| Change Request | 7,247 | 11.2h | 4,858 | 67.0% |
| Problem | 167 | 6.2h | 21 | 12.6% |
Alerts get the fastest response at 1.0 hour on average, likely due to automated monitoring triggers. Change Requests take the longest at 11.2 hours but paradoxically have the highest SLA compliance (67.0%), suggesting these SLAs have generous deadlines. Problem tickets are rare (167 total) but have the worst SLA compliance at 12.6%.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[ticket_type_name],
"AvgFirstResponseHours", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]),
"TicketCount", COUNTROWS('BI_Autotask_Tickets'),
"SLAMetCount", SUM('BI_Autotask_Tickets'[first_response_met])
)
ORDER BY [TicketCount] DESCHow average first response time has changed over the past 19 months, from July 2024 to January 2026.
Response times spiked to 20.9 hours in May 2025 and 15.6 hours in June, then dropped sharply. January 2026 shows the best performance at 2.0 hours. The May-June spike suggests either a staffing gap or a surge of complex tickets during that period.
EVALUATE
FILTER(
SUMMARIZECOLUMNS(
'BI_Common_Dim_Date'[year],
'BI_Common_Dim_Date'[month],
'BI_Common_Dim_Date'[month_name],
"AvgFirstResponseHours", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]),
"TicketCount", COUNTROWS('BI_Autotask_Tickets')
),
[TicketCount] > 0
)
ORDER BY 'BI_Common_Dim_Date'[year] ASC, 'BI_Common_Dim_Date'[month] ASCThe data tells a clear story. Only 52.9% of all tickets meet their first response SLA, which means nearly half your tickets get a late first touch. For an MSP, that directly impacts client satisfaction and contract renewals.
The priority breakdown reveals an inverted problem. P1 tickets perform well at 0.8 hours average, which is expected since critical issues get immediate attention. But P2 and P3 tickets, which make up 16,503 tickets combined, have SLA compliance rates of 35.7% and 34.4%. These are the tickets that sit in queues waiting for pickup while engineers handle P1s and P4s.
The monthly trend shows response times are improving. After peaking at 20.9 hours in May 2025, the trend has dropped to 2.0 hours in January 2026. Whether this is sustainable depends on whether the underlying cause (staffing, tooling, process) has been fixed.
At 9.6 hours average and only 35.7% SLA compliance, P2 tickets are the biggest gap between expected urgency and actual response. These 1,788 tickets are high-priority but appear to fall between the cracks: not urgent enough for P1 immediate response, but too few to attract queue-level attention. Consider auto-escalation rules that bump P2 tickets if no first response within 2 hours.
With 14,715 tickets and a 34.4% SLA rate, P3 is responsible for the largest absolute number of missed SLAs (9,650 tickets). The P90 of 16.0 hours means 10% of P3 tickets wait more than half a day. Review queue assignment rules: tickets landing in low-staffed queues may account for the tail.
Response times hit 20.9 hours in May and 15.6 hours in June before recovering. If this was a staffing issue (vacation, turnover), build redundancy into scheduling for Q2 2026. If it was a ticket surge (onboarding, incident), the alert routing may need tuning.
Average first response dropped to 2.0 hours in January 2026, down from 4.6 hours in December. If this is driven by process changes, document what worked. If it is a seasonal low-volume effect (2,164 tickets vs 4,562 in January 2025), the improvement may not hold as volumes return.
First response time is the number of hours between when a ticket is created and when the first billable note, time entry, or status change is recorded. It measures how quickly a client hears back after submitting a request.
Many tickets have a first_response_duration_hours of 0, meaning the first response happened within the same hour or was recorded simultaneously with ticket creation (common with alerts and automated acknowledgments). The average is pulled up by a smaller number of tickets with long response times.
The first_response_met column is an integer (0 or 1) set by Autotask based on whether the first response happened before the SLA deadline configured for that ticket's priority and SLA template. A value of 1 means the deadline was met.
Yes. Every collapsible DAX section in this report contains the exact query that produced the data. Open Power BI Desktop, connect to your Proxuma dataset, open the DAX query view (View > DAX query), paste the query, and run it. You will get the same results with your own live data.
The MCP server automatically anonymizes sensitive data before it reaches the AI. Client names, resource names, and contact details are replaced with aliases (Client_A, Resource_1). You can restore real names locally using the mapping file at ~/.powerbi-mcp/sessions/latest/mapping.json.
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