“SLA Compliance Overview: First Response and Resolution Targets”
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What is the overall SLA compliance for first response and resolution targets?

SLA Compliance Overview: First Response and Resolution Targets

Which clients are meeting SLA targets, which are falling short, and where the bottleneck sits. Generated by AI via Proxuma Power BI MCP server.

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
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SLA Compliance Overview: First Response and Resolution Targets

Which clients are meeting SLA targets, which are falling short, and where the bottleneck sits. 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

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 › SLA Compliance Overview: First Respon...
What you can measure in this report
Summary Metrics
SLA Compliance by Client — Top 15 by Volume
SLA Compliance by Service Queue
SLA Compliance by Priority Level
Worst Performers — Clients Below 50% on Both SLA Metrics
Analysis
What Should You Do With This Data?
Frequently Asked Questions
Total Tickets
First Response Met
Resolution Met
Avg CSAT
AI-Generated Power BI Report
SLA Compliance Overview:
First Response and Resolution Targets

Which clients are meeting SLA targets, which are falling short, and where the bottleneck sits. Generated by AI via Proxuma Power BI MCP server.

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
52.9%
35,715 of 67,521 met target
First Response Met
63.5%
42,892 of 67,521 met target
Resolution Met
6.3 hours
Mean first response time
Avg CSAT
18.0 hours
Mean resolution time
52.9% 35,715
First Response Met
63.5% 42,892
Resolution Met
87.7% CSAT
Customer Satisfaction
View DAX Query — Summary Metrics 
EVALUATE ROW("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), "AvgFirstResponseHours", AVERAGE('BI_Autotask_Tickets'[first_response_duration_hours]), "AvgResolutionHours", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]))
What are these DAX queries? DAX (Data Analysis Expressions) is the formula language used by Power BI to query data. Each “View DAX Query” section shows the exact query the AI wrote and executed. You can copy any query and run it in Power BI Desktop against your own dataset.
2.0 SLA Compliance by Client — Top 15 by Volume

First response and resolution compliance for the 15 clients with the most tickets. Color coding: below 50% = red, 50-70% = amber, above 70% = green.

#ClientTicketsFirst ResponseResolutionStatus
1Client A6,38128.8%50.4%Critical
2Client B5,45870.3%66.7%Watch
3Client C5,29063.5%64.7%Watch
4Client D2,77539.6%69.2%Watch
5Client E2,37673.6%72.5%Good
6Client F2,36490.2%92.0%Excellent
7Client G2,18031.7%52.1%Critical
8Client H1,80330.7%47.3%Critical
9Client I1,75848.9%67.5%Watch
10Client J1,72837.8%71.2%Watch
11Client K1,68422.3%27.8%Critical
12Client L1,62963.7%70.8%Good
13Client M1,48176.4%85.6%Excellent
14Client N1,31731.3%47.4%Critical
15Client O1,00261.0%92.3%Good
View DAX Query — SLA Compliance by Client 
EVALUATE
VAR _Top15 =
    TOPN(15,
        SUMMARIZE(BI_Autotask_Tickets,
            BI_Autotask_Companies[company_name]),
        CALCULATE(COUNTROWS(BI_Autotask_Tickets)), DESC)
RETURN
ADDCOLUMNS(_Top15,
    "Tickets", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
    "FR_Met_Pct", DIVIDE(
        CALCULATE(COUNTROWS(BI_Autotask_Tickets),
            BI_Autotask_Tickets[first_response_met] + 0 = 1),
        CALCULATE(COUNTROWS(BI_Autotask_Tickets))),
    "Res_Met_Pct", DIVIDE(
        CALCULATE(COUNTROWS(BI_Autotask_Tickets),
            BI_Autotask_Tickets[resolution_met] + 0 = 1),
        CALCULATE(COUNTROWS(BI_Autotask_Tickets)))
)
ORDER BY [Tickets] DESC
3.0 SLA Compliance by Service Queue

First response and resolution rates broken down by queue, showing where operational bottlenecks exist

Servicedesk (31,378 tickets)
FR 63.6%
Res 59.2%
Monitoring (17,082 tickets)
FR 34.0%
Res 74.8%
L2 Support (7,889 tickets)
FR 53.7%
Res 72.9%
Projects (2,316 tickets)
FR 43.4%
Res 39.4%
First Response Met Resolution Met
QueueTicketsFirst ResponseResolutionGap
Servicedesk31,37863.6%59.2%+4.4pp
Monitoring17,08234.0%74.8%-40.8pp
L2 Support7,88953.7%72.9%-19.2pp
Projects2,31643.4%39.4%+4.0pp
View DAX Query — SLA by Queue 
EVALUATE
ADDCOLUMNS(
    SUMMARIZE(BI_Autotask_Tickets,
        BI_Autotask_Tickets[queue_name]),
    "Tickets", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
    "FR_Met_Pct", DIVIDE(
        CALCULATE(COUNTROWS(BI_Autotask_Tickets),
            BI_Autotask_Tickets[first_response_met] + 0 = 1),
        CALCULATE(COUNTROWS(BI_Autotask_Tickets))),
    "Res_Met_Pct", DIVIDE(
        CALCULATE(COUNTROWS(BI_Autotask_Tickets),
            BI_Autotask_Tickets[resolution_met] + 0 = 1),
        CALCULATE(COUNTROWS(BI_Autotask_Tickets)))
)
ORDER BY [Tickets] DESC
4.0 SLA Compliance by Priority Level

How well each priority tier meets its SLA targets, ordered by ticket volume

PriorityTicketsFirst ResponseResolutionRisk
P4 Low30,41561.1%63.4%265 breaches
Service/Change15,58456.5%57.4%Watch
P3 Normal (Mon.)14,71534.4%61.3%FR bottleneck
P3 Normal5,01952.3%92.3%Good
P2 High1,78835.7%56.6%Urgent
View DAX Query — SLA by Priority 
EVALUATE
ADDCOLUMNS(
    SUMMARIZE(BI_Autotask_Tickets,
        BI_Autotask_Tickets[priority_name]),
    "Tickets", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
    "FR_Met_Pct", DIVIDE(
        CALCULATE(COUNTROWS(BI_Autotask_Tickets),
            BI_Autotask_Tickets[first_response_met] + 0 = 1),
        CALCULATE(COUNTROWS(BI_Autotask_Tickets))),
    "Res_Met_Pct", DIVIDE(
        CALCULATE(COUNTROWS(BI_Autotask_Tickets),
            BI_Autotask_Tickets[resolution_met] + 0 = 1),
        CALCULATE(COUNTROWS(BI_Autotask_Tickets)))
)
ORDER BY [Tickets] DESC
5.0 Worst Performers — Clients Below 50% on Both SLA Metrics

Clients where both first response and resolution compliance fall below the 50% threshold, requiring immediate attention

ClientTicketsFirst ResponseResolutionCombined ScoreAction
Client K 1,684 22.3% 27.8% 25.1% Escalation required
Client A 6,381 28.8% 50.4% 39.6% Largest client at risk
Client H 1,803 30.7% 47.3% 39.0% Review needed
Client N 1,317 31.3% 47.4% 39.4% Review needed
Client G 2,180 31.7% 52.1% 41.9% Watch closely
View DAX Query — Worst Performers 
EVALUATE
VAR _ByClient =
    ADDCOLUMNS(
        SUMMARIZE(BI_Autotask_Tickets,
            BI_Autotask_Companies[company_name]),
        "Tickets", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
        "FR_Pct", DIVIDE(
            CALCULATE(COUNTROWS(BI_Autotask_Tickets),
                BI_Autotask_Tickets[first_response_met] + 0 = 1),
            CALCULATE(COUNTROWS(BI_Autotask_Tickets))),
        "Res_Pct", DIVIDE(
            CALCULATE(COUNTROWS(BI_Autotask_Tickets),
                BI_Autotask_Tickets[resolution_met] + 0 = 1),
            CALCULATE(COUNTROWS(BI_Autotask_Tickets)))
    )
RETURN
FILTER(_ByClient,
    [FR_Pct] < 0.50 || [Res_Pct] < 0.50)
ORDER BY [FR_Pct] ASC
6.0 Analysis

The headline numbers tell a clear story: first response compliance at 52.9% is the primary failure point. Resolution compliance sits at 63.5%, which is better but still below where most MSPs aim to be. The 10.6 percentage-point gap between the two metrics means that once your team gets to a ticket, they tend to resolve it. The problem is getting to it in the first place.

Client K is the most severe case. With 1,684 tickets and a combined SLA compliance of just 25.1%, this account is receiving a fundamentally different level of service than the rest of your portfolio. Only 22.3% of tickets get a first response on time, and only 27.8% are resolved within the SLA window. At this level, the SLA is effectively meaningless for this client.

Client A is your largest account by ticket volume at 6,381 tickets, and their first response rate is just 28.8%. This is your highest-risk combination: big contract, poor delivery. The resolution rate of 50.4% is marginally better, but half the tickets still miss the target. If this client runs their own compliance tracking, they already know the numbers are bad.

The Monitoring queue shows a 40.8 percentage-point gap between first response (34.0%) and resolution (74.8%). This pattern is typical of auto-created monitoring alerts where the ticket exists before anyone looks at it. The SLA clock starts at ticket creation, but a human may not acknowledge it for hours. The fix is usually operational: either adjust the SLA policy for monitoring tickets or set up auto-acknowledgment for known alert types.

P2 High priority tickets are at 35.7% first response compliance. These are your most urgent tickets, and nearly two-thirds miss the initial response window. For P2 tickets, every minute counts. A 35.7% compliance rate on high-priority work is a staffing or routing issue, not a one-off problem.

On the positive side, Client F at 90.2% / 92.0% and Client M at 76.4% / 85.6% show that strong SLA performance is achievable with your current processes and team. The gap between these clients and the worst performers suggests the issue is inconsistent execution rather than a broken system.

7.0 What Should You Do With This Data?

5 priorities based on the findings above

1

Investigate Client K immediately: 22.3% first response is a contract risk

Client K has the worst SLA compliance in your portfolio across both metrics. With 1,684 tickets and a 22.3% first response rate, the gap is too wide to be explained by a few bad weeks. Pull the ticket history and check for patterns: are these tickets landing in the wrong queue? Is there a specific technician bottleneck? Is the SLA configuration correct for this client's contract? At 27.8% resolution compliance, this client is receiving emergency-level service as their baseline.

2

Fix first response workflows for Client A: your largest account at 28.8%

Client A generates 6,381 tickets, more than any other account. A 28.8% first response rate on that volume means roughly 4,543 tickets missed the initial response deadline. Before the next QBR, pull the first response timestamps and identify where the delay occurs. Is it dispatch time? Technician acknowledgment? A misrouted queue? Fixing this one client would materially improve your overall portfolio number.

3

Address the Monitoring queue acknowledgment gap

The Monitoring queue has a 40.8 percentage-point gap between first response (34.0%) and resolution (74.8%). This is almost certainly caused by auto-generated alerts where the SLA clock starts at ticket creation but no one acknowledges the ticket for hours. Two options: set up auto-acknowledgment rules for known alert categories (patch completed, backup succeeded, threshold normalized), or adjust the SLA policy so the clock starts at manual triage rather than auto-creation.

4

Review P2 High priority routing and staffing

P2 tickets at 35.7% first response compliance is a serious operational gap. These tickets have the tightest SLA windows by definition, and missing them has an outsized impact on client trust. Check whether P2 tickets are routed to a dedicated queue with guaranteed staffing. If they end up in the general servicedesk queue, they compete with P4 tickets for attention. 1,788 P2 tickets at this compliance rate means over 1,150 missed first responses on your most critical work.

5

Use Client F and Client M as benchmarks for process improvement

Client F at 90.2% / 92.0% and Client M at 76.4% / 85.6% prove that your team can deliver strong SLA compliance when conditions are right. Study what makes these accounts different. Is it the ticket volume? The queue assignment? The technician team? The SLA targets themselves? Whatever works for Client F should be replicated across the accounts that are failing. Start by comparing ticket routing, average response times, and dispatch rules between your best and worst performers.

8.0 Frequently Asked Questions
What counts as "first response met" in this report?

Autotask tracks whether the first response to a ticket occurred before the SLA deadline. The first_response_met field is a binary flag (1 = met, 0 = missed). This report calculates the percentage of tickets where that flag equals 1. The SLA deadline itself is set per client contract and priority level in Autotask.

Why is first response compliance lower than resolution compliance?

This pattern is common in MSPs. First response has a shorter SLA window (often 1-4 hours) and depends on someone picking up the ticket immediately. Resolution windows are longer (often 8-24 hours) and start from the same moment. Once a ticket is acknowledged and assigned, teams tend to resolve it within the remaining time. The bottleneck is almost always the initial pickup.

How are monitoring tickets different from regular tickets?

Monitoring tickets are auto-created by your RMM tool when an alert fires. The SLA clock starts at the moment the ticket is created, not when a technician sees it. This creates an inherent disadvantage for first response compliance because the ticket may sit in a queue for hours before anyone triages it. Resolution compliance is higher because once acknowledged, the underlying issue is often resolved quickly or auto-resolves.

What should our SLA compliance target be?

Most MSP contracts define SLA targets between 80% and 95% compliance. Industry benchmarks suggest that top-performing MSPs maintain above 85% for first response and above 90% for resolution. At 52.9% first response and 63.5% resolution, there is significant room for improvement. A realistic near-term goal would be 70% first response and 80% resolution within one quarter.

Can I run this report against my own data?

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. Your SLA field names and configurations must match the Proxuma semantic model.

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