“Preventable SLA Breaches”
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How many SLA breaches were actually preventable?

Preventable SLA Breaches

A data-driven analysis of preventable sla breaches from your Power BI environment, with breakdowns and actionable findings.

Built from: Autotask PSA
How this report was made
1
Autotask PSA
Multiple data sources combined
2
Proxuma Power BI
Pre-built MSP semantic model, 50+ measures
3
AI via MCP
Claude or ChatGPT writes DAX queries, executes them, formats output
4
This Report
KPIs, breakdowns, trends, recommendations
Ready in < 15 min
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Preventable SLA Breaches

This report analyzes preventable sla breaches using data from Autotask PSA.

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

Time saved
Pulling per-client SLA data from PSA manually takes hours. This report delivers the breakdown in minutes.
Client-level clarity
Portfolio averages mask the clients getting poor service. This report surfaces the specific accounts that need attention.
Contract evidence
Concrete SLA data per client gives you proof points for renewals, pricing adjustments, or staffing conversations.
Report categorySLA & Service Performance
Data sourceAutotask PSA · Datto RMM · Datto Backup · Microsoft 365 · SmileBack · HubSpot · IT Glue
RefreshReal-time via Power BI
Generation timeUnder 15 minutes
AI requiredClaude, ChatGPT or Copilot
AudienceService delivery managers, operations leads
Where to find this in Proxuma
Power BI › SLA › Preventable SLA Breaches
What you can measure in this report
Summary Metrics
SLA Performance Summary
Ticket Volume by Company
Priority Distribution
Monthly Ticket Trend
Analysis
Recommended Actions
Frequently Asked Questions
TOTAL TICKETS
TOP CLIENT
FIRST RESPONSE SLA
RESOLUTION SLA
AI-Generated Power BI Report
Preventable SLA Breaches

A data-driven analysis of preventable sla breaches from your Power BI environment, with breakdowns and actionable findings.

Demo Report: This report uses synthetic data to demonstrate AI-generated insights from Proxuma Power BI. The structure, DAX queries, and analysis reflect real MSP data patterns.
1.0 Summary Metrics
TOTAL TICKETS
360
Resolution SLA missed
TOP CLIENT
199
55,3% — FR was met
FIRST RESPONSE SLA
80,1%
Portfolio measure
RESOLUTION SLA
90,2%
Portfolio measure
52.9% Target: 80%
First Response SLA (35,715 of 67,521)
63.5% Target: 85%
Resolution SLA (42,892 of 67,521)
View DAX Query — Summary query 
EVALUATE ROW("Tickets", COUNTROWS('BI_Autotask_Tickets'), "Breaches", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0), "Preventable", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0, 'BI_Autotask_Tickets'[first_response_met] + 0 = 1), "FRTMetPct", [Tickets - First Response Met %], "ResMetPct", [Tickets - Resolution Met %])
What are these DAX queries? DAX (Data Analysis Expressions) is the formula language Power BI uses to query data. Each collapsible section below shows the exact query the AI wrote and ran. You can copy any query and run it in Power BI Desktop against your own dataset.
1.0 SLA Performance Summary

First response and resolution SLA compliance rates

52.9% Target: 80%
First Response SLA (35,715 of 67,521)
63.5% Target: 85%
Resolution SLA (42,892 of 67,521)
MetricMetTotalRate
First Response SLA35,71567,52152.9%
Resolution SLA42,89267,52163.5%
View DAX Query — SLA Performance Summary query 
EVALUATE ROW("Total", 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))
2.0 Ticket Volume by Company

Clients ranked by total ticket count from the demo dataset

Wilson-Murphy
1,002
Burke, Armstrong and Morg
1,629
Lopez-Reyes
1,317
Ford, Mclean and Robinson
1,684
Lewis LLC
1,758
Thompson, Contreras and R
1,803
Stephens-Martinez
1,481
Rivers, Rogers and Mitche
6,381
Blanchard-Glenn
2,364
Martin Group
2,775
CompanyTickets
Wilson-Murphy1,002
Burke, Armstrong and Morgan1,629
Lopez-Reyes1,317
Ford, Mclean and Robinson1,684
Lewis LLC1,758
Thompson, Contreras and Rios1,803
Stephens-Martinez1,481
Rivers, Rogers and Mitchell6,381
Blanchard-Glenn2,364
Martin Group2,775
Price-Gomez2,180
Little Group5,290
Wall PLC2,376
Craig-Huynh5,458
Ramos Group1,728
View DAX Query — Ticket Volume by Company query 
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Tickets'[company_name], "Tickets", COUNTROWS('BI_Autotask_Tickets')), [Tickets], DESC)
3.0 Priority Distribution

Ticket mix by priority level

21.8%
P3 - Medium (14,715)
45.0%
P4 - Laag (30,415)
7.4%
P1 - Kritisch (5,019)
2.6%
P2 - Hoog (1,788)
23.1%
Service/Change req. (15,584)
PriorityBreachesPreventable% Preventable
P4 - Low26517465,7%
P3 - Medium681420,6%
P2 - High15640,0%
Service/Change req.9333,3%
P1 - Critical3266,7%
View DAX Query — Priority Distribution query 
EVALUATE ADDCOLUMNS(SUMMARIZE(FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[resolved_due_age_days] > 0), 'BI_Autotask_Tickets'[priority_name]), "Breaches", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0), "Preventable", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0, 'BI_Autotask_Tickets'[first_response_met] + 0 = 1)) ORDER BY [Breaches] DESC
4.0 Monthly Ticket Trend

Monthly ticket volume over the observed period

7,0575,7784,4993,2201,941 3,4786,6132,164 202502202504202506202508202510202512202601
MonthTicketsBreachesPreventable
Jan 20262.164235121
Dec 20252.9406049
Nov 20253.3273514
Oct 20254.013176
Sep 20254.56353
Aug 20253.60722
Jul 20256.61311
Jun 20253.65121
May 20253.63911
Apr 20254.34110
Jan 20254.56211
View DAX Query — Monthly Ticket Trend query 
EVALUATE FILTER(ADDCOLUMNS(VALUES('BI_Common_Dim_Date'[year_month]), "Tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')), "Breaches", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0), "Preventable", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolved_due_age_days] > 0, 'BI_Autotask_Tickets'[first_response_met] + 0 = 1)), [Breaches] > 0) ORDER BY 'BI_Common_Dim_Date'[year_month] DESC
6.0 Analysis

What the data is telling us

Across 39,226 total records, the distribution is heavily concentrated. Wilson-Murphy alone accounts for 2.6% of all volume (1,002 records). This kind of concentration is worth monitoring: if one client consistently dominates workload, it may signal scope creep, inadequate preventive maintenance, or a pricing mismatch.

Looking at the monthly trend, ticket volume has moved downward over the observed period, from 3,478 to 2,164. A downward trend may reflect improved automation, better documentation, or reduced client activity.

SLA compliance sits at 52.9% first response and 63.5% resolution. There is room for improvement here. Focus on the queues and priorities with the lowest compliance to find quick wins.

7.0 Recommended Actions
!

1. First Response SLA Below Target

At 52.9%, first response SLA is below the 80% target. Review queue routing and auto-assignment rules to reduce initial response time.

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2. Investigate Wilson-Murphy Volume

Wilson-Murphy generates the most activity. Review whether this aligns with their contract scope and SLA tier.

✓

3. Schedule Recurring Review

Set up a weekly or monthly review of preventable sla breaches metrics. Trends matter more than snapshots. Use the DAX queries in this report as your starting point.

8.0 Frequently Asked Questions
What data sources does the Preventable SLA Breaches report use?

This report pulls data from PSA through the Proxuma Power BI integration, using DAX queries against the live data model.

How often is this data refreshed?

The underlying Power BI dataset refreshes daily. Reports can be regenerated at any time for the latest figures.

Can I customize this preventable sla breaches report?

Yes. Proxuma reports are fully customizable. You can modify the DAX queries, add new sections, or adjust the analysis to match your specific MSP needs.

Proxuma Power BI · AI-Generated Report · proxuma.io/powerbi Generated in < 15 minutes via MCP · March 2026
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