Which priority levels hit their resolution deadline, which queues fall behind, and which clients see the most breaches. Generated by AI via Proxuma Power BI MCP server.
Which priority levels hit their resolution deadline, which queues fall behind, and which clients see the most breaches. 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 priority levels hit their resolution deadline, which queues fall behind, and which clients see the most breaches. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("Tickets Total", COUNTROWS('BI_Autotask_Tickets'), "Resolution Met %", [Tickets - Resolution Met %], "First Response Met %", [Tickets - First Response Met %], "Same Day Resolution %", [Tickets - Same Day Resolution %])Resolution compliance rate per priority, ranked from highest to lowest. This is the answer to the question.
| Priority | Tickets | FRT Met | Res Met |
|---|---|---|---|
| P4 - Laag | 30,415 | 83.5% | 90.6% |
| Service/Change req. | 15,584 | 97.3% | 97.5% |
| P3 - Medium | 14,715 | 55.2% | 83.8% |
| P1 - Kritisch | 5,019 | 82.4% | 94.0% |
| P2 - Hoog | 1,788 | 68.6% | 71.8% |
EVALUATE TOPN(10, FILTER(SUMMARIZECOLUMNS('BI_Autotask_Tickets'[priority_name], "Tickets", COUNTROWS('BI_Autotask_Tickets'), "FRTMet", [Tickets - First Response Met %], "ResMet", [Tickets - Resolution Met %]), [Tickets] > 0), [Tickets], DESC) ORDER BY [Tickets] DESCSLA compliance per service queue, showing which teams resolve within deadline and which fall behind
| Queue | Tickets | FRT Met | Res Met |
|---|---|---|---|
| L1 Support | 31,378 | 88.5% | 95.6% |
| Centralized Services | 17,082 | 64.7% | 91.6% |
| L2 Support | 7,889 | 82.3% | 88.0% |
| Merged Tickets | 4,999 | 78.1% | 92.4% |
| Technical Alignment | 2,316 | 74.6% | 62.8% |
| Customer succes | 804 | 72.3% | 59.5% |
| Interne IT | 793 | 33.4% | 55.7% |
| Onsite support | 705 | 76.6% | 56.0% |
| Professional Services | 546 | 71.6% | 52.0% |
| Administration | 327 | 59.2% | 61.9% |
EVALUATE TOPN(10, FILTER(SUMMARIZECOLUMNS('BI_Autotask_Tickets'[queue_name], "Tickets", COUNTROWS('BI_Autotask_Tickets'), "FRTMet", [Tickets - First Response Met %], "ResMet", [Tickets - Resolution Met %]), [Tickets] >= 200), [Tickets], DESC) ORDER BY [Tickets] DESCTop 10 clients by ticket volume, ranked from worst to best SLA compliance. Clients below 60% need attention.
| # | Client | Tickets | Resolved in SLA | Missed | SLA % | Status |
|---|---|---|---|---|---|---|
| 1 | Martinez Contreras Rios | 1,803 | 853 | 950 | 47.3% | Critical |
| 2 | Holt Bradley Fowler | 994 | 471 | 523 | 47.4% | Critical |
| 3 | Rivers Rogers Mitchell | 6,381 | 3,216 | 3,165 | 50.4% | Critical |
| 4 | Price-Gomez | 2,180 | 1,135 | 1,045 | 52.1% | Critical |
| 5 | Foster Inc | 5,290 | 3,423 | 1,867 | 64.7% | Below Target |
| 6 | Patterson Hood Perez | 5,458 | 3,642 | 1,816 | 66.7% | Below Target |
| 7 | Martin Group | 1,758 | 1,187 | 571 | 67.5% | Below Target |
| 8 | Hernandez Ltd | 2,775 | 1,921 | 854 | 69.2% | Acceptable |
| 9 | Nelson Taylor Hicks | 1,728 | 1,231 | 497 | 71.2% | On Target |
| 10 | Wall PLC | 2,376 | 1,723 | 653 | 72.5% | On Target |
EVALUATE
VAR _TopClients =
TOPN(10,
VALUES('BI_Autotask_Tickets'[company_name]),
CALCULATE(COUNTROWS('BI_Autotask_Tickets')), DESC)
RETURN
ADDCOLUMNS(
_TopClients,
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"res_met", CALCULATE(
COUNTROWS(
FILTER('BI_Autotask_Tickets',
[resolution_met] + 0 = 1))),
"res_missed", CALCULATE(
COUNTROWS(
FILTER('BI_Autotask_Tickets',
NOT([resolution_met] + 0 = 1))))
)
ORDER BY [res_met] / [tickets] ASCThe overall resolution SLA rate of 63.5% means more than one in three tickets misses its resolution deadline. That number alone signals a systemic issue, not isolated incidents.
P3 - Normaal is the only priority performing well, hitting 92.3% SLA compliance across 5,019 tickets. Every other priority level sits between 56% and 64%. The gap between P3 - Normaal at 92.3% and P2 - Hoog at 56.6% is particularly concerning: high-priority tickets are resolved within SLA less often than normal-priority ones. That suggests the SLA deadlines for P2 may be too aggressive, or those tickets get stuck in escalation paths that eat into the resolution window.
P4 - Laag is the biggest contributor to missed SLA in absolute numbers. With 30,415 tickets and a 63.4% hit rate, that is 11,129 tickets missing their deadline. The sheer volume means even a small percentage improvement here would move the global number significantly.
The Projects queue at 39.4% is by far the worst performer. Project tickets often have longer lifecycles and dependencies on external parties, which may explain some of the gap. But a sub-40% SLA rate across 2,316 tickets means the resolution targets for this queue need revisiting, or project tickets should be excluded from SLA tracking entirely.
The Servicedesk queue handles 31,378 tickets at a 59.2% SLA rate. Because it carries 46% of total volume, improving this queue by even 5 percentage points would lift the global number from 63.5% to roughly 66%.
At the client level, Rivers Rogers Mitchell stands out: 6,381 tickets with only 50.4% resolved in SLA. That is 3,165 missed deadlines for a single client. Martinez Contreras Rios and Holt Bradley Fowler are worse by percentage (47.3% and 47.4%), but Rivers Rogers Mitchell generates the most breach volume. If that client sees their SLA data in a QBR, the conversation will be difficult.
5 priorities based on the findings above
P2 tickets have the lowest SLA compliance at 56.6%, yet these are the tickets that matter most to clients. Pull the 775 missed tickets and look at time-to-escalation, queue transfers, and whether technicians are picking them up within the first hour. If the SLA window is 4 hours and tickets routinely sit in dispatch for 2, the fix is operational. If the window is genuinely too short, adjust it and document the change.
The Servicedesk handles 31,378 tickets at 59.2% SLA. Because of its volume, a 5-point improvement here would lift the global rate more than fixing any other queue. Look at the most common ticket types that miss SLA in this queue and check if they are being routed correctly. Misrouted tickets that bounce between queues burn resolution time.
At 39.4%, the Projects queue is failing by design. Project tickets have dependencies, waiting periods, and multi-step resolution paths that do not fit standard SLA windows. Either give project tickets a separate SLA policy with longer resolution windows, or exclude them from SLA tracking and measure them on milestone completion instead.
Rivers Rogers Mitchell generates 3,165 SLA breaches across 6,381 tickets. Martinez Contreras Rios sits at 47.3%. Before their next QBR, build a targeted breakdown: which ticket types breach most, which queues handle their work, and what the root causes are. Walking into a QBR with a fix plan is better than getting caught off guard by a client who has been tracking their own SLA numbers.
360 tickets are currently open past their resolution deadline. That number will keep growing until they are resolved or escalated. Run a daily triage of active breaches sorted by age. The oldest ones are the most visible to clients. Clearing the backlog reduces the breach count and prevents compounding: a ticket that breaches resolution SLA often drags other metrics (client satisfaction, response SLA) down with it.
A ticket counts as "resolution met" when it was resolved (status changed to Complete) before the SLA deadline set by its priority level in Autotask. The resolution_met field is a boolean (stored as int64) that Proxuma Power BI calculates from the ticket's resolution timestamp and its SLA due date.
"Missed SLA" includes all tickets (open or closed) where the resolution deadline was not met. "Active breaches" counts only tickets that are still open and past their resolution deadline right now. These are the ones you can still act on. The 24,629 missed includes historical closed tickets; the 360 active breaches are the current backlog.
P3 - Normaal tickets typically have SLA windows that match the actual effort required. They are standard break-fix tickets with predictable resolution paths. Higher priorities (P2) often involve issues that take longer to diagnose, while lower priorities (P4) and service requests tend to sit in queue longer because they are deprioritized in favor of urgent work.
Industry benchmarks vary, but most mature MSPs target 80% or above for resolution SLA across all priorities. For P1/P2 (critical/high), the target is often 90%+. At 63.5% overall, there is significant room for improvement. The P3 - Normaal rate of 92.3% shows what is achievable when SLA windows and workflows align.
Yes. Add a date filter to any DAX query by wrapping the CALCULATE in a FILTER on BI_Autotask_Tickets[complete_date] or [create_date]. For client-specific views, add a filter on BI_Autotask_Tickets[company_name]. You can also slice by queue, technician, or ticket type using the same pattern.
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.
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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