First response and resolution SLA rates across P1 through P4, plus Service/Change requests. Year-over-year trends and priority-specific concerns. Generated by AI via Proxuma Power BI MCP server.
First response and resolution SLA rates across P1 through P4, plus Service/Change requests. Year-over-year trends and priority-specific concerns. 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
First response and resolution SLA rates across P1 through P4, plus Service/Change requests. Year-over-year trends and priority-specific concerns. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"OverallFRPct", DIVIDE(
COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[first_response_met] + 0 = 1)),
COUNTROWS('BI_Autotask_Tickets')
),
"OverallResPct", DIVIDE(
COUNTROWS(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[resolution_met] + 0 = 1)),
COUNTROWS('BI_Autotask_Tickets')
),
"TotalTickets", COUNTROWS('BI_Autotask_Tickets')
)First response and resolution rates side by side for each priority level
| Metric | Value |
|---|---|
| Resolution Met | 90.2% |
| First Hour Fix | 16.1% |
| Same-Day | 30.0% |
| Closure | 98.8% |
EVALUATE ROW("ResolutionMet", [Tickets - Resolution Met %], "FirstHourFix", [Tickets - First Hour Fix %], "SameDayRes", [Tickets - Same Day Resolution %], "ClosureRate", [Tickets - Closure Rate %], "TotalTickets", [Tickets - Count - Created])How quickly are tickets getting their first response, broken down by priority?
First response SLA is the weaker metric across all priorities. The overall rate sits at 52.9%, meaning almost half of all tickets miss their first response target. The breakdown by priority reveals some sharp contrasts.
P3 - Normaal has the worst first response rate at 34.4%. With nearly 15,000 tickets, this is not a niche problem. One in three P3 tickets gets a timely first response. The remaining two-thirds wait longer than the SLA allows.
P1 - Kritiek is only slightly better at 35.7%. These are your highest-severity tickets, and roughly two out of three are not getting a response within the SLA window. That is a significant service gap for critical issues.
P4 - Laag actually leads at 61.1%, which makes sense: low-priority tickets tend to have more generous SLA windows. Still, nearly 40% of them miss the target.
Resolution SLA rates per priority level
Resolution performance is stronger than first response across the board, but there is one clear outlier: P2 - Hoog at 92.3%. That is an excellent resolution rate, especially given that these are high-priority tickets. The team consistently resolves them within the target window.
The other priorities cluster between 56% and 64%. P1 - Kritiek resolution at 56.7% is concerning. Nearly half of critical tickets are resolved outside the SLA window. Combined with the 35.7% first response rate, P1 tickets are getting a late start and a late finish.
Service/Change requests sit at 57.4% resolution, the second lowest. These tickets often involve approvals and handoffs, which can slow resolution. But if the SLA targets account for that, 57% is still too low.
EVALUATE
ADDCOLUMNS(
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[priority_name],
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"FirstResponseMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[first_response_met] + 0 = 1),
"ResolutionMet", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1)
),
"FirstResponsePct", DIVIDE([FirstResponseMet], [TotalTickets]),
"ResolutionPct", DIVIDE([ResolutionMet], [TotalTickets])
)
ORDER BY [TotalTickets] DESCHow each priority's SLA rates have changed from 2024 to 2026
The yearly trends reveal patterns that the overall numbers hide. Several priorities show dramatic shifts that deserve attention.
| Priority | Count | % |
|---|---|---|
| P4 | 30,415 | 45.0% |
| Service/Change | 15,584 | 23.1% |
| P3 | 14,715 | 21.8% |
| P1 | 5,019 | 7.4% |
| P2 | 1,788 | 2.6% |
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[priority_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'))The two most urgent patterns in this dataset
P1 Kritiek first response has collapsed. In 2024, 63.6% of critical tickets got a timely first response. By 2026, that number is 22.4%. That is a 65% relative decline in two years. The positive side: resolution actually improved from 50.7% to 75.3%, so the team eventually gets to these tickets. But the initial acknowledgment is consistently late, which impacts client confidence during emergencies.
P2 Hoog first response dropped from 86% to 5.5%. This is the steepest decline in the dataset. Resolution remains rock-solid at 90-93%, but the first response has nearly disappeared. This suggests a process change: tickets are being resolved quickly but the initial response timestamp is not being recorded, or the auto-response was disabled. Alternatively, the SLA target for P2 first response was tightened without adjusting dispatch behavior. Either way, the 5.5% number needs investigation before drawing operational conclusions.
Service/Change requests dropped off a cliff in 2026. Both first response (10.3%) and resolution (11.2%) fell dramatically after a strong 2025. This could indicate a data issue with partial-year 2026 data, or a genuine workflow breakdown. Verify whether 2026 data covers the full period before acting.
This disconnect is too large to be purely operational. Resolution at 90.9% proves the team handles P2 tickets well. The first response number likely reflects a configuration or tracking issue. Check whether P2 tickets have an auto-acknowledgment enabled, and verify that the SLA timer starts at the right trigger point. If the SLA target was changed in 2025, the historic comparison is misleading.
Critical tickets are the ones that clients remember. A 22.4% first response rate means roughly four out of five P1 tickets start with a late acknowledgment. Resolution improved to 75.3%, which is good, but late first contact on a critical issue erodes trust fast. Review on-call staffing, dispatch rules, and whether P1 tickets are landing in the right queue immediately.
P3 tickets often get deprioritized when P1 and P2 volume spikes. But with nearly 15,000 tickets at this level, the sheer volume of missed SLAs creates a constant background of client dissatisfaction. P3 first response has been stuck in the mid-20s since 2025, suggesting a structural capacity issue rather than a temporary spike.
A drop from 86% to 5.5% with stable resolution is almost certainly a tracking or configuration issue. Check the SLA timer trigger, auto-acknowledgment rules, and whether the P2 first response target was changed. Fix the root cause before the next reporting cycle so you can trust the data going forward.
Critical tickets need a sub-15-minute first touch. If your current dispatch flow routes P1 tickets to the same general queue as everything else, they wait. Consider a dedicated P1 handler during business hours or an escalation bot that pages the on-call engineer immediately. The resolution improvement shows the team can handle the work once they get to it.
34.4% first response on your highest-volume "normal" priority means the service desk is consistently behind. Look at ticket arrival patterns by hour and day. If P3 tickets pile up during peak hours while agents handle P1/P2, you may need a tiered staffing model or an auto-triage system that handles initial acknowledgment.
Both FR and resolution dropped to roughly 10-11% in 2026. Before you restructure the workflow, verify the data: does the 2026 dataset cover the same timeframe as previous years? If this is partial-year data (Q1 only), the numbers may normalize. If it is a full dataset, something changed in how service requests are processed.
P2 resolution at 92.3% proves that high SLA compliance is achievable. Study the dispatch and escalation patterns for P2 tickets and see what makes them different. The same approach might be applicable to P1 and P3 resolution workflows, which currently sit in the 50-60% range.
First response met means the ticket received its initial response (acknowledgment, reply, or status update) within the SLA target time defined for that priority level in Autotask. The field is a boolean flag on each ticket. A value of 1 means the SLA was met; 0 means it was missed.
This kind of gap usually points to a configuration issue. Common causes: the auto-acknowledgment rule was disabled, the SLA timer was changed to start at a different trigger point, or the first response SLA target was tightened without updating dispatch procedures. Check your Autotask SLA settings for P2 tickets before drawing operational conclusions.
Priority levels come directly from Autotask: P1 Kritiek (Critical), P2 Hoog (High), P3 Normaal (Normal), P4 Laag (Low), and Service/Change requests. Each priority has its own SLA targets for first response and resolution. The definitions and target times are set in your Autotask SLA configuration.
Yes. The DAX queries can be extended with date filters using YEAR('BI_Autotask_Tickets'[create_date]) or FILTER expressions. For client-specific analysis, add a filter on 'BI_Autotask_Tickets'[company_name]. Copy the query from any DAX toggle, add your filter, and run it in Power BI Desktop.
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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