Generated by AI via Proxuma Power BI MCP server. First response time and SLA compliance by ticket priority, from P1 Critical down to Service Requests.
Generated by AI via Proxuma Power BI MCP server. First response time and SLA compliance by ticket priority, from P1 Critical down to Service Requests.
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
Generated by AI via Proxuma Power BI MCP server. First response time and SLA compliance by ticket priority, from P1 Critical down to Service Requests.
EVALUATE
ROW(
"Total_Tickets", COUNTROWS('BI_Autotask_Tickets'),
"Avg_First_Response_Hours",
AVERAGEX(
FILTER('BI_Autotask_Tickets',
NOT(ISBLANK('BI_Autotask_Tickets'[first_response_date_time]))
),
'BI_Autotask_Tickets'[first_response_time_hours]
),
"SLA_Met_Count",
COUNTROWS(FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[first_response_sla_met] = TRUE()
))
)All priorities with first response data. Sorted by ticket volume descending.
| Metric | Value |
|---|---|
| Avg First Response | 6.25h |
| FR SLA Met | 52.9% |
| Avg Resolution | 18.04h |
| Total Tickets | 67,521 |
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "AvgFirstRespHrs", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "FRMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "AvgResolutionHrs", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))The data shows a pattern that is common in MSPs with strong critical ticket escalation processes but weaker general queue management. P1 critical tickets average 0.83 hours to first response, which is fast. The team clearly mobilizes when something is flagged as a system-down situation. But P2 high tickets average 9.59 hours, eleven times longer. That gap is the problem. Clients who raise a high-priority issue and wait most of a working day for a first response notice that discrepancy even if they cannot articulate it in SLA terms.
P3 medium tickets at 8.87 hours average response and only 34.4% SLA compliance represent the largest volume risk. With 14,715 tickets in this band, more than 9,600 of them are missing first response SLA. The sheer volume makes this the biggest source of client exposure in the portfolio. P4 low tickets achieve the best SLA compliance at 61.1%, which is still below most contractual targets but reflects a more manageable queue with lighter priority pressure.
Service requests and change requests average 7.74 hours at 56.5% compliance. These are typically non-urgent by nature, but the compliance rate still matters for managed contract clients who expect acknowledgement within a defined window regardless of urgency.
4 actions based on the data above
P2 tickets average 9.59 hours to first response and only 639 out of 1,788 meet SLA. This is the priority band where clients feel the biggest gap between expectation and reality. High-priority work should get a response within 1 to 2 hours under most managed contracts. A 9.59-hour average suggests P2 tickets are sitting in the general queue without triage differentiation from P3 work.
With 14,715 P3 tickets at 34.4% compliance, more than 9,600 tickets failed first response SLA. At this volume, these are not occasional misses. They are a structural pattern. Review whether P3 SLA targets are realistic given current staffing, or whether queue routing needs to change to prevent P3 tickets from being consistently delayed by higher-priority work.
P1 critical tickets get a response in under an hour on average. This shows the escalation process is functioning. The challenge is carrying that urgency discipline into the P2 band, where the same clients expect near-critical response but the queue does not treat it that way.
Even with a 0.83-hour average, only half of P1 tickets meet their SLA commitment. This likely means that SLA targets for P1 are tight (15 to 30 minutes in many contracts) and some critical tickets, particularly those raised outside business hours or during high-load periods, are still breaching. Review the distribution of P1 response times and check whether after-hours coverage is the primary driver.
Autotask records the timestamp when the first internal note or time entry is logged against a ticket. First response time is the difference between the ticket creation timestamp and that first activity timestamp. Automated responses and system-generated notes may or may not count depending on your Autotask configuration. Verify your SLA definition settings to confirm what Autotask treats as a qualifying first response.
P2 tickets often have tighter SLA targets than P3 in most contracts. If P2 SLA requires response within 1 hour and P3 within 4 hours, both priorities can show similar actual response times while P2 has worse compliance. Check your SLA configuration in Autotask to confirm the target window for each priority band.
Yes. Ask the AI to add a filter to the DAX query for a specific client, contract type, or date range. For example: “Show me first response time by priority for managed service clients only, last 90 days.” The query structure stays the same; the filter condition changes.
Yes. Connect Proxuma Power BI to your Autotask PSA account, add an AI tool 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.
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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