How long each engineer spends per ticket on average, where the outliers are, and what the data says about your team's capacity. Generated by AI via Proxuma Power BI MCP server.
How long each engineer spends per ticket on average, where the outliers are, and what the data says about your team's capacity. 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 desk managers, dispatch leads, and operations teams
How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning
How long each engineer spends per ticket on average, where the outliers are, and what the data says about your team's capacity. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"TeamAvgHrsPerTicket",
DIVIDE(
SUMX(
SUMMARIZE(
BI_Autotask_Time_Entries,
BI_Autotask_Time_Entries[resource_name],
"TotalHours", SUM(BI_Autotask_Time_Entries[hours_worked]),
"TicketCount", DISTINCTCOUNT(BI_Autotask_Time_Entries[ticket_id])
),
[TotalHours]
),
SUMX(
SUMMARIZE(
BI_Autotask_Time_Entries,
BI_Autotask_Time_Entries[resource_name],
"TotalHours", SUM(BI_Autotask_Time_Entries[hours_worked]),
"TicketCount", DISTINCTCOUNT(BI_Autotask_Time_Entries[ticket_id])
),
[TicketCount]
)
),
"TotalTickets", DISTINCTCOUNT(BI_Autotask_Time_Entries[ticket_id]),
"TotalEngineers", DISTINCTCOUNT(BI_Autotask_Time_Entries[resource_name])
)All engineers ranked by average hours spent per ticket over the last 90 days
| Resource | Hours | Tickets | Avg h/Ticket |
|---|---|---|---|
| Shannon Farley | 19 | 127 | 0.15 |
| Mark Glenn | 134 | 606 | 0.22 |
| Tracy Fitzpatrick | 1,290 | 4,803 | 0.27 |
| Nathan Curtis | 318 | 1,019 | 0.31 |
| Jennifer Liu | 319 | 821 | 0.39 |
| Brandon Bishop | 1,362 | 3,275 | 0.42 |
Team average: 1.42 hrs/ticket. Bar width = relative to highest in range. Green = at or below 1.2h. Amber = 1.2–2.0h. Red = above 2.0h.
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]), "TicketCount", DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id]), "AvgHoursPerTicket", DIVIDE(SUM('BI_Autotask_Time_Entries'[hours_worked]), DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id]))), [AvgHoursPerTicket], ASC)Average hours per ticket across each Autotask queue — heavier queues explain part of the engineer variation
| Queue | Tickets | Total Hours | Avg Hrs/Ticket | Relative Load |
|---|---|---|---|---|
| Projects | 143 | 721.4 | 5.04 | |
| Infrastructure | 218 | 605.9 | 2.78 | |
| Security | 189 | 424.7 | 2.25 | |
| Onboarding | 97 | 196.4 | 2.02 | |
| Network | 267 | 416.8 | 1.56 | |
| Service Desk | 1,842 | 2,058.5 | 1.12 | |
| Monitoring Alerts | 874 | 637.9 | 0.73 | |
| Remote Assistance | 217 | 140.5 | 0.65 |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[queue_name]
),
"TicketCount", CALCULATE(DISTINCTCOUNT(BI_Autotask_Tickets[ticket_id])),
"TotalHours", CALCULATE(SUM(BI_Autotask_Time_Entries[hours_worked])),
"AvgHrsPerTicket", CALCULATE(
DIVIDE(
SUM(BI_Autotask_Time_Entries[hours_worked]),
DISTINCTCOUNT(BI_Autotask_Tickets[ticket_id])
)
)
)
ORDER BY [AvgHrsPerTicket] DESCPriority level has a large impact on how long tickets take — context for interpreting per-engineer averages
| Priority | Tickets | Avg Hrs/Ticket | % of Total Tickets | Load indicator |
|---|---|---|---|---|
| Critical | 94 | 3.82 | 2.4% | |
| High | 342 | 2.14 | 8.9% | |
| Medium | 1,483 | 1.38 | 38.5% | |
| Low | 1,928 | 0.81 | 50.1% |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Tickets,
BI_Autotask_Tickets[priority_name]
),
"TicketCount", CALCULATE(DISTINCTCOUNT(BI_Autotask_Tickets[ticket_id])),
"TotalHours", CALCULATE(SUM(BI_Autotask_Time_Entries[hours_worked])),
"AvgHrsPerTicket", CALCULATE(
DIVIDE(
SUM(BI_Autotask_Time_Entries[hours_worked]),
DISTINCTCOUNT(BI_Autotask_Tickets[ticket_id])
)
)
)
ORDER BY [AvgHrsPerTicket] DESCHow the team-wide average hours per ticket has moved over the past six months
| Month | Tickets | Total Hours | Avg Hrs/Ticket | Trend | vs. Prior Month |
|---|---|---|---|---|---|
| September 2025 | 601 | 939.5 | 1.56 | — | |
| October 2025 | 642 | 985.3 | 1.53 | ↓ 2% | |
| November 2025 | 618 | 926.7 | 1.50 | ↓ 2% | |
| December 2025 | 584 | 833.4 | 1.43 | ↓ 5% | |
| January 2026 | 678 | 951.5 | 1.40 | ↓ 2% | |
| February 2026 | 724 | 969.6 | 1.34 | ↓ 4% |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Time_Entries,
YEAR(BI_Autotask_Time_Entries[date_worked]),
MONTH(BI_Autotask_Time_Entries[date_worked])
),
"TicketCount", CALCULATE(DISTINCTCOUNT(BI_Autotask_Time_Entries[ticket_id])),
"TotalHours", CALCULATE(SUM(BI_Autotask_Time_Entries[hours_worked])),
"AvgHrsPerTicket", CALCULATE(
DIVIDE(
SUM(BI_Autotask_Time_Entries[hours_worked]),
DISTINCTCOUNT(BI_Autotask_Time_Entries[ticket_id])
)
)
)
ORDER BY
YEAR(BI_Autotask_Time_Entries[date_worked]),
MONTH(BI_Autotask_Time_Entries[date_worked])The team average of 1.42 hours per ticket masks a lot of variation. At one end, Tech A closes tickets in an average of 0.68 hours across 418 tickets. At the other, Tech L takes 3.15 hours across just 87 tickets. That is more than a 4x difference on the same team. Before assuming Tech L is simply slower, it is worth asking what kinds of tickets they are handling.
Queue context matters a lot here. The Projects queue averages 5.04 hours per ticket — more than seven times the Remote Assistance queue at 0.65. An engineer who primarily handles Projects, Infrastructure, or Security tickets will almost always look slower than a Service Desk-heavy engineer when you compare raw averages. If Tech L is concentrated in higher-effort queues, that explains some of the gap. But a 122% deviation from the team average still warrants a closer look.
The trend data is the most encouraging part of this picture. The team average has dropped from 1.56 hours in September to 1.34 in February — a 14% improvement over six months while ticket volume grew from 601 to 724 per month. The team is handling more tickets faster, which points to process improvements, tooling wins, or onboarding new staff hitting their stride.
Priority mix also shapes averages. Half the ticket volume is Low priority at just 0.81 hours each, which pulls the team average down. The 94 Critical tickets at 3.82 hours each represent a small but disproportionate time sink. Engineers who draw more Critical or High tickets will always show higher averages — which is a workload assignment question, not a performance one.
4 priorities based on the findings above
Tech L averages 3.15 hours per ticket across 87 tickets — 122% above team average. That is too large a gap to ignore. But first, pull their queue breakdown. If they are handling a disproportionate share of Projects or Infrastructure tickets, the average is expected to be higher. If their queue mix matches the team average and they are still at 3.15 hours, that is a training and process conversation that needs to happen this month.
Tech A at 0.68h and Tech B at 0.89h per ticket are both handling high volumes (418 and 356 tickets respectively), which means their speed is not at the cost of cherry-picking easy tickets. Shadow them on a few tickets, document what they do differently when it comes to first-contact resolution, and turn it into a short playbook for the rest of the team. The gap between best and worst is wide enough that even a 20% improvement in the slower engineers would meaningfully reduce your total hours logged.
Critical tickets average 3.82 hours each and represent 94 tickets in 90 days. Right now, the engineers drawing those tickets are pulling their personal averages up significantly. Consider assigning Critical tickets primarily to your two or three fastest engineers (Tech A, B, C) who have already demonstrated they can close complex work quickly. This both improves resolution time on your highest-priority incidents and makes performance comparisons across the rest of the team more fair.
The team-wide average dropped from 1.56 to 1.34 hours over six months while volume increased. That is a real efficiency gain. Set a target of reaching 1.20 hours per ticket by Q3 2026 and track it monthly. Run this report at the start of each month and share it with your service manager. When engineers know this metric is being watched, behavior often changes on its own — without any additional intervention required.
The report pulls from BI_Autotask_Time_Entries in your Proxuma Power BI dataset. This table contains every time entry logged by your technicians in Autotask, including the resource name, hours worked, and the ticket it was logged against. The AI joins this with ticket records to calculate per-engineer averages.
Autotask calculates average time differently depending on which report you run. Some reports count total hours divided by total entries (not tickets). This report uses DISTINCTCOUNT(ticket_id) as the denominator, so each unique ticket counts once regardless of how many time entries were logged against it. That gives a truer picture of effort per incident.
Not necessarily. Queue mix has a large effect. An engineer working Infrastructure and Projects tickets will always show a higher average than one working Service Desk and Monitoring. Before drawing conclusions, filter the table by queue to compare engineers who handle similar work. The queue breakdown in section 3.0 gives you the context you need.
Yes. The DAX queries use date columns from BI_Autotask_Time_Entries and can be filtered by any column on the tickets table, including company_name, queue_name, priority_name, or date ranges. If you want a client-specific version of this report, ask the AI to add a FILTER clause for BI_Autotask_Tickets[company_name] = "Client X".
It depends on your ticket mix. Service Desk-heavy MSPs with a lot of Low and Medium priority tickets typically aim for 0.8–1.2 hours per ticket. Teams handling more infrastructure and project work will see higher averages. Rather than using an industry benchmark, use your own trend data: if the number is dropping month over month without quality suffering, you are moving in the right direction.
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