AI-GENERATED REPORT

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How are tickets distributed across service queues?

Tickets by Queue

Q: Why is the Merged queue so large?

When tickets are merged in Autotask, the surviving ticket often lands in a Merged queue. At 4,999 tickets this represents duplicate detection working as intended. If the number seems high, check whether your merge workflow is also capturing unrelated tickets.

Q: Should I consolidate low-volume queues?

It depends on why the queue exists. Some queues may exist for workflow automation or compliance reasons. Review each low-volume queue with the service desk manager before removing it. The goal is fewer queues without losing the routing logic those queues provide.

A breakdown of 67,521 tickets across 16 Autotask queues, ranked by volume and average effort per ticket.

Built from: Autotask PSA

How this report was made

Autotask PSA

Multiple data sources combined

Proxuma Power BI

Pre-built MSP semantic model, 50+ measures

AI via MCP

Claude or ChatGPT writes DAX queries, executes them, formats output

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Tickets by Queue

A breakdown of 67,521 tickets across 16 Autotask queues, ranked by volume and average effort per ticket.

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 desk managers, dispatch leads, and operations teams

How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning

Time saved

Manual ticket analysis requires exporting data and building pivot tables. This report does it automatically.

Queue health

Stuck tickets, aging backlogs, and escalation patterns become visible at a glance.

Process improvement

Data-driven decisions about routing, staffing, and escalation rules.

Report categoryTicketing & Helpdesk

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 desk managers, dispatch leads

Where to find this in Proxuma

Power BI › Ticketing › Tickets by Queue

Get this report for your MSP

What you can measure in this report

Key Metrics

Ticket Volume by Queue

Volume Distribution

Average Hours per Ticket by Queue

Effort vs. Volume

Long-Tail Queues

Queue Concentration

Analysis

What Should You Do With This Data?

Frequently Asked Questions

Total Tickets

Top 3 Concentration

Power BI · AI-Generated Report

Data source: Autotask PSA

Generated: March 2026

Scope: All queues · All time

Sources: Autotask PSA

Tickets by Queue

A breakdown of 67,521 tickets across 16 Autotask queues, ranked by volume and average effort per ticket.

Demo Report: This report uses synthetic data from a sample Autotask environment. Connect your own PSA to Proxuma Power BI to generate this report with your real ticket data.

1.0 Key Metrics

Total Tickets

67,521

Across 16 queues

Top 3 Concentration

83.5%

High concentration

Highest Avg Hours

3.88h

Consultancy queue

Bottom 6 Queues

681

1.0% of total volume

View DAX Query — Key Metrics

EVALUATE
SUMMARIZECOLUMNS(
    BI_Autotask_Tickets[queue_name],
    "TotalTickets", COUNT(BI_Autotask_Tickets[ticket_id]),
    "AvgHoursPerTicket", DIVIDE(
        SUM(BI_Autotask_Tickets[worked_hours]),
        COUNT(BI_Autotask_Tickets[ticket_id]),
        0
    )
)
ORDER BY [TotalTickets] DESC

2.0 Ticket Volume by Queue

All 16 queues ranked by total ticket count

Queue	Tickets	% of Total
L1 Support	31,378	46.5%
Centralized Services	17,082	25.3%
L2 Support	7,889	11.7%
Merged Tickets	4,999	7.4%
Technical Alignment	2,316	3.4%
Customer succes	804	1.2%
Interne IT	793	1.2%
Onsite support	705	1.0%

View DAX Query — Ticket Volume by Queue

EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[queue_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'))

3.0 Volume Distribution

Visual breakdown of ticket concentration across the top queues

Servicedesk

31,378 (46.5%)

Monitoring

17,082 (25.3%)

L2 Support

7,889 (11.7%)

Merged

4,999 (7.4%)

Projects

2,316

Other (11)

3,857

Concentration insight: The top 3 queues (Servicedesk, Monitoring, L2 Support) handle 56,349 tickets, or 83.5% of total volume. The remaining 13 queues share 11,172 tickets. Any staffing or process change in those top 3 queues will have an outsized impact on overall service delivery.

View DAX Query — Volume Distribution

EVALUATE
VAR _QueueVolume =
    SUMMARIZE(
        BI_Autotask_Tickets,
        BI_Autotask_Tickets[queue_name],
        "Tickets", COUNT(BI_Autotask_Tickets[ticket_id])
    )
VAR _Total = COUNTROWS(BI_Autotask_Tickets)
VAR _Top3 =
    SUMX(
        TOPN(3, _QueueVolume, [Tickets], DESC),
        [Tickets]
    )
RETURN
ROW(
    "TotalTickets", _Total,
    "Top3Tickets", _Top3,
    "Top3Pct", FORMAT(DIVIDE(_Top3, _Total, 0), "0.0%"),
    "RemainingQueues", COUNTROWS(_QueueVolume) - 3,
    "RemainingTickets", _Total - _Top3
)

4.0 Average Hours per Ticket by Queue

Queues ranked by the average engineer time spent per ticket. Higher values indicate more complex or labor-intensive work.

#	Queue	Avg Hours / Ticket	Total Tickets	Effort Level
1	Consultancy	3.875h	546	High
2	Projects	3.028h	2,316	High
3	Onsite	2.396h	705	Medium
4	Customer succes	1.474h	804	Medium
5	L2 Support	1.278h	7,889	Standard
6	Administration	0.974h	327	Standard
7	Monitoring	0.833h	17,082	Low
8	Servicedesk	0.572h	31,378	Low
9	Merged	0.508h	4,999	Low
10	Interne IT	0.415h	793	Low

View DAX Query — Average Hours per Ticket

EVALUATE
ADDCOLUMNS(
    SUMMARIZE(
        BI_Autotask_Tickets,
        BI_Autotask_Tickets[queue_name]
    ),
    "TotalTickets", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
    "AvgHoursPerTicket", CALCULATE(
        DIVIDE(
            SUM(BI_Autotask_Tickets[worked_hours]),
            COUNT(BI_Autotask_Tickets[ticket_id]),
            0
        )
    )
)
ORDER BY [AvgHoursPerTicket] DESC

5.0 Effort vs. Volume

Comparing total estimated hours consumed per queue (tickets x avg hours) to identify where engineer time actually goes

Servicedesk

17,948h

31,378 tix

Monitoring

14,229h

17,082 tix

L2 Support

10,082h

7,889 tix

Projects

7,013h

2,316 tix

Merged

2,540h

4,999 tix

Consultancy

2,116h

546 tix

Onsite

1,689h

705 tix

Key takeaway: Projects ranks 5th by ticket count but 4th by total hours consumed. With only 2,316 tickets at 3.028h each, it absorbs an estimated 7,013 hours. Compare that to Merged, which handles 4,999 tickets but only consumes an estimated 2,540 hours at 0.508h per ticket. Volume alone does not tell you where the work goes.

View DAX Query — Estimated Total Hours per Queue

EVALUATE
ADDCOLUMNS(
    SUMMARIZE(
        BI_Autotask_Tickets,
        BI_Autotask_Tickets[queue_name]
    ),
    "TotalTickets", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
    "AvgHoursPerTicket", CALCULATE(
        DIVIDE(
            SUM(BI_Autotask_Tickets[worked_hours]),
            COUNT(BI_Autotask_Tickets[ticket_id]),
            0
        )
    ),
    "TotalEstimatedHours", CALCULATE(
        SUM(BI_Autotask_Tickets[worked_hours])
    )
)
ORDER BY [TotalEstimatedHours] DESC

6.0 Long-Tail Queues

Queues with fewer than 250 tickets. Candidates for consolidation or review.

Queue	Tickets	% of Total	Assessment
Post Sale	209	0.3%	Review needed
Networking	193	0.3%	Review needed
Sales	107	0.2%	Consolidation candidate
Recurring (Parked)	98	0.1%	Holding queue
Pre-sales	45	0.1%	Consolidation candidate
Compliancy	29	0.0%	Consolidation candidate

Combined total: These 6 queues process 681 tickets, 1.0% of all volume. Sales, Pre-sales, and Compliancy together handle 181 tickets. If these queues do not have a specific routing or compliance reason to exist, merging them into a parent queue would simplify reporting and reduce routing errors.

View DAX Query — Long-Tail Queues

EVALUATE
FILTER(
    ADDCOLUMNS(
        SUMMARIZE(
            BI_Autotask_Tickets,
            BI_Autotask_Tickets[queue_name]
        ),
        "TotalTickets", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
        "PctOfTotal", DIVIDE(
            CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id])),
            COUNTROWS(BI_Autotask_Tickets), 0
        )
    ),
    [TotalTickets] < 250
)
ORDER BY [TotalTickets] DESC

7.0 Queue Concentration

How workload distributes across four tiers

Servicedesk alone

Top 3 queues combined

Bottom 6 queues combined

View DAX Query — Queue Concentration

EVALUATE
VAR _QueueVolume =
    ADDCOLUMNS(
        SUMMARIZE(BI_Autotask_Tickets, BI_Autotask_Tickets[queue_name]),
        "Tickets", CALCULATE(COUNT(BI_Autotask_Tickets[ticket_id]))
    )
VAR _Total = COUNTROWS(BI_Autotask_Tickets)
VAR _Top1 = MAXX(_QueueVolume, [Tickets])
VAR _Top3 = SUMX(TOPN(3, _QueueVolume, [Tickets], DESC), [Tickets])
VAR _Bottom6 = SUMX(TOPN(6, _QueueVolume, [Tickets], ASC), [Tickets])
RETURN
ROW(
    "Top1Pct", DIVIDE(_Top1, _Total, 0),
    "Top3Pct", DIVIDE(_Top3, _Total, 0),
    "Bottom6Pct", DIVIDE(_Bottom6, _Total, 0)
)

8.0 Analysis

The single most striking pattern in this data is concentration. Servicedesk alone handles 46.5% of all tickets, roughly 31,400 out of 67,500. Add Monitoring and L2 Support and those three queues account for 83.5%. Everything else is, comparatively, noise.

That concentration is not necessarily a problem. Servicedesk is designed to be the front door. Monitoring generates automated alerts. L2 Support catches escalations. The question is whether the staffing and tooling behind those three queues matches the workload they carry. If Servicedesk is understaffed relative to its 46.5% share, response times and SLA compliance will suffer across nearly half of all incoming work.

Average hours per ticket tells a different story. Consultancy tickets average 3.875 hours each, and Projects averages 3.028 hours. These queues handle relatively few tickets (546 and 2,316 respectively) but consume a disproportionate amount of engineer time. Projects alone absorbs an estimated 7,013 hours, more than Merged (2,540h) despite processing fewer tickets. If you are planning capacity, counting tickets alone will mislead you.

The long tail is worth questioning. Six queues process fewer than 250 tickets combined, totaling 681 tickets or 1% of all volume. Sales (107 tickets), Pre-sales (45), and Compliancy (29) each see fewer than two tickets per week on average. Unless these queues exist for compliance or routing reasons that justify their overhead, consolidating them into broader categories would simplify queue management and make reporting cleaner.

The Merged queue at 4,999 tickets is also worth investigating. "Merged" typically means tickets combined from other queues. At 7.4% of all volume, that is a significant number of tickets ending up in a catch-all. If the merge process is inconsistent, you may be losing visibility into where those tickets originated.

9.0 What Should You Do With This Data?

5 priorities based on the findings above

Validate that Servicedesk staffing matches its 46.5% workload share

Nearly half of all tickets flow through Servicedesk. If your Servicedesk team is not proportionally sized, you are creating a bottleneck at the front door. Pull SLA compliance and first-response time for the Servicedesk queue specifically and compare it against your targets. At 0.572h per ticket, these are not complex issues. Speed of response is what matters here.

Investigate automation potential in Monitoring (17,082 tickets)

Monitoring generates 25.3% of all tickets at 0.833h per ticket. Many monitoring tickets follow predictable patterns: alert fires, engineer checks, clears or escalates. If even 20% of those 17,082 tickets could be auto-resolved or auto-cleared, that frees up an estimated 2,844 hours of engineer time per year. Look at which monitoring alert types have the highest auto-close rate and build automation around those first.

Track the Merged queue for routing transparency

4,999 tickets in a "Merged" queue represent 7.4% of volume. Merged tickets lose their original queue context, which makes it harder to measure performance by queue accurately. Audit a sample of merged tickets to understand where they came from and whether the merge process preserves the original queue as metadata. If it does not, you are losing reporting fidelity.

Evaluate whether the bottom 6 queues need to exist as separate queues

Sales (107), Pre-sales (45), Compliancy (29), Recurring/Parked (98), Post Sale (209), and Networking (193) handle 681 tickets combined. That is less than 1% of total volume. Each separate queue adds routing complexity and can lead to tickets sitting in low-traffic queues without timely attention. Review whether these can be consolidated without losing the workflow or compliance benefits they were created for.

Use hours-per-ticket data for project and consultancy pricing

Consultancy at 3.875h and Projects at 3.028h per ticket give you real cost-of-service data. If you are pricing consultancy or project work per ticket or per engagement, these averages should inform your rates. 546 consultancy tickets at 3.875h each means roughly 2,116 hours of engineer time going to consultancy work. Make sure that time is being billed or accounted for.

10.0 Frequently Asked Questions

Where does the queue data come from?

Queue assignments come from Autotask PSA. Every ticket has a queue_name field that indicates which team or workflow owns it. Proxuma Power BI pulls this data through the Autotask connector. The AI then groups and counts tickets by queue, and calculates average hours using the worked_hours field on each ticket.

What does "average hours per ticket" measure?

It is the total worked_hours recorded on all tickets in a queue, divided by the number of tickets in that queue. This includes all time entries logged by engineers against those tickets. A high number indicates complex or time-consuming work. A low number suggests quick resolutions or automated handling.

Why is the "Merged" queue so large?

When tickets are merged in Autotask (for example, duplicate reports of the same issue), the surviving ticket often lands in a Merged queue or retains a merged status. At 4,999 tickets, this represents duplicate detection working as intended. If the number seems high, check whether your merge workflow is also capturing unrelated tickets.

Should I consolidate low-volume queues?

It depends on why the queue exists. Some queues (Compliancy, Pre-sales) may exist for workflow automation or compliance reasons. Others may be leftovers from a previous team structure. Review each low-volume queue with the service desk manager before removing it. The goal is fewer queues without losing the routing logic those queues provide.

Can I run this report filtered to a specific time period?

Yes. Add a date filter to the DAX queries using the create_date or complete_date column on BI_Autotask_Tickets. For example, filtering to the last 12 months would show recent queue distribution rather than historical totals. This is useful for spotting whether queue volumes are shifting over time.

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 ticket data, and produces a report like this in under fifteen minutes.

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