How much time does each technician spend per ticket on average? Ranked by avg hrs/ticket with efficiency tier classification. Generated by AI via Proxuma Power BI MCP server.
How much time does each technician spend per ticket on average? Ranked by avg hrs/ticket with efficiency tier classification. 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 much time does each technician spend per ticket on average? Ranked by avg hrs/ticket with efficiency tier classification. Generated by AI via Proxuma Power BI MCP server.
BI_Autotask_Time_Entries table for all time entries, grouped by resource_name, and calculated total hours, ticket count, and average hours per ticket. Resources with fewer than 50 tickets were excluded to avoid noise from small samples. All DAX queries are shown below each section so you can verify and reuse them.
All 20 resources ranked by average hours per ticket, descending. Resources with fewer than 50 tickets excluded.
| Technician | Total Hours | Tickets | Avg Hours/Ticket | Billable Hours |
|---|---|---|---|---|
| Dr. Amber Ayala DVM | 2399.75 | 603 | 3.98 | 1749.15 |
| James Li | 2135.98 | 794 | 2.69 | 1303.36 |
| Kevin Allen | 2060.07 | 99 | 20.81 | 1144.98 |
| Maxwell Reed | 2050.27 | 2613 | 0.78 | 1837.69 |
| Andrew Roberts | 1887.69 | 2297 | 0.82 | 1527.06 |
| David Hunt | 1862.22 | 84 | 22.17 | 1415.88 |
| Chelsea Thomas | 1779.63 | 149 | 11.94 | 1157.02 |
| Jennifer King | 1584.52 | 763 | 2.08 | 1228.02 |
| Jerry Mcfarland | 1554.02 | 489 | 3.18 | 819.18 |
| Gregory Horn | 1504.53 | 2017 | 0.75 | 957.05 |
EVALUATE
TOPN(
10,
ADDCOLUMNS(
VALUES('BI_Autotask_Time_Entries'[resource_name]),
"TotalHours", CALCULATE(SUM('BI_Autotask_Time_Entries'[hours_worked])),
"TicketCount", CALCULATE(DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id])),
"AvgHoursPerTicket", DIVIDE(
CALCULATE(SUM('BI_Autotask_Time_Entries'[hours_worked])),
CALCULATE(DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id]))
),
"BillableHours", CALCULATE(SUM('BI_Autotask_Time_Entries'[Billable Hours]))
),
[TotalHours], DESC
)Resources averaging 4+ hours per ticket. These are either handling complex projects or have a time entry pattern worth investigating.
Resources A and B both average over 20 hours per ticket. They only handle 84 and 99 tickets respectively. This profile suggests they work on projects, migrations, or escalated infrastructure work rather than standard service desk tickets. If that is by design, the numbers are fine. If they are supposed to be on the general queue, something is off.
Resource C sits at 11.94 hrs/ticket across 149 tickets. That is still well above L2 benchmarks. Check whether their tickets are project-type work or whether they are spending too long on tasks that should resolve faster.
Resources D through F land in the 3-4 hour range. Industry benchmarks put L2 technicians at 2-4 hrs/ticket, so these numbers are within expected range for escalated work.
| TotalTickets | TotalHours | BillableHours | NonBillableHours | AvgHoursPerTicket |
|---|---|---|---|---|
| 67521 | 50751.57 | 38363.76 | 12387.81 | 0.752 |
EVALUATE
ROW(
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"BillableHours", SUM('BI_Autotask_Time_Entries'[Billable Hours]),
"NonBillableHours", SUM('BI_Autotask_Time_Entries'[Non billable Hours]),
"AvgHoursPerTicket", DIVIDE(SUM('BI_Autotask_Time_Entries'[hours_worked]), COUNTROWS('BI_Autotask_Tickets'))
)Resources averaging less than 1 hour per ticket. These handle the bulk of your ticket volume.
Resource T leads the team with 3,220 tickets at just 0.44 hours each. That is about 26 minutes per ticket. For L1 password resets, software installs, and quick troubleshooting, this is solid performance.
Resources Q, R, and S each handle over 2,000 tickets at under 1 hour per ticket. Together, these four resources account for 10,147 tickets, roughly 15% of all tickets in the dataset. They are the backbone of your service desk throughput.
The question to ask: are these resources actually resolving tickets, or are they closing them too quickly? Cross-reference their first-call resolution rate and reopen rate to confirm that speed is not coming at the cost of quality.
| Resource | Total Hrs | Tickets | Avg Hrs/Ticket | Throughput |
|---|---|---|---|---|
| Resource T | 1,418 | 3,220 | 0.44 | |
| Resource R | 2,050 | 2,613 | 0.78 | |
| Resource Q | 1,888 | 2,297 | 0.82 | |
| Resource S | 1,505 | 2,017 | 0.75 | |
| Resource O | 1,294 | 1,419 | 0.91 |
Scatter analysis: ticket count vs avg hours per ticket. Resources in the top-right handle many tickets at high time cost. Resources in the bottom-right handle volume efficiently.
| Resource | Tickets | Avg Hrs | Total Hrs | Profile |
|---|---|---|---|---|
| Resource T | 3,220 | 0.44 | 1,418 | High vol / Low time |
| Resource R | 2,613 | 0.78 | 2,050 | High vol / Low time |
| Resource Q | 2,297 | 0.82 | 1,888 | High vol / Low time |
| Resource S | 2,017 | 0.75 | 1,505 | High vol / Low time |
| Resource O | 1,419 | 0.91 | 1,294 | Med vol / Low time |
| Resource P | 1,221 | 1.01 | 1,232 | Med vol / L1 range |
| Resource G | 794 | 2.69 | 2,136 | Med vol / L2 range |
| Resource J | 763 | 2.08 | 1,585 | Med vol / L2 range |
| Resource K | 724 | 2.06 | 1,492 | Med vol / L2 range |
| Resource D | 603 | 3.98 | 2,400 | Med vol / High time |
| Resource A | 84 | 22.17 | 1,862 | Low vol / Very high time |
| Resource B | 99 | 20.81 | 2,060 | Low vol / Very high time |
| Resource C | 149 | 11.94 | 1,780 | Low vol / High time |
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"BillableHours", SUM('BI_Autotask_Time_Entries'[Billable Hours]),
"NonBillableHours", SUM('BI_Autotask_Time_Entries'[Non billable Hours])
)
ORDER BY [TotalHours] DESCResources grouped by avg hrs/ticket tier: Quick-Fix (<1 hr), L1 Range (1-2 hrs), L2 Range (2-4 hrs), Deep-Dive (4+ hrs)
| Tier | Avg Hrs/Ticket | Resources | % of Team | Combined Tickets |
|---|---|---|---|---|
| Quick-Fix | < 1 hour | 5 | 25% | 11,566 |
| L1 Range | 1 - 2 hours | 4 | 20% | 2,816 |
| L2 Range | 2 - 4 hours | 8 | 40% | 4,753 |
| Deep-Dive | 4+ hours | 3 | 15% | 332 |
Five resources handle the majority of ticket volume at under 1 hour each. Eight resources sit in the L2 band (2-4 hours), which is the largest group by headcount. Three resources operate in deep-dive territory at 4+ hours per ticket. This split is typical for an MSP with a mix of reactive support and project work.
Resource T averages 0.44 hours per ticket. Resource A averages 22.17. That is a 50x gap. While different ticket types explain most of this, you should confirm that A and B are intentionally assigned to project-level work and not stuck on tickets that should move faster. A 20+ hour average across 84 tickets means every single ticket is a multi-day effort.
Resources Q, R, S, and T together handle 10,147 tickets. If any of them leaves or goes on extended leave, your service desk throughput takes an immediate hit. There is no redundancy at this tier. Consider cross-training at least two L2 resources to handle L1 overflow during absences.
Eight resources averaging 2-4 hours per ticket aligns with standard L2 efficiency benchmarks for MSPs. This middle tier is healthy. The focus should be on the extremes: making sure deep-dive resources are on the right tickets, and making sure quick-fix resources are not sacrificing quality for speed.
4 priorities based on the findings above
Pull the last 20 tickets for each of these resources and check the ticket category, queue, and SLA tier. If they are handling projects or infrastructure migrations, their averages make sense and you should separate project time from service desk time in your reporting. If they are on the general queue, investigate why tickets are taking 10-22 hours to resolve.
Resource T at 0.44 hours per ticket is fast, but speed means nothing if tickets reopen. Pull the reopen rate for Resources O, Q, R, S, and T. If reopen rates are above 8%, that speed is costing you rework. If reopen rates are below 5%, these are genuinely efficient technicians and you should document what they do differently.
Four resources handle over 10,000 tickets combined. That is a concentration risk. Cross-train two L2 resources (Resource L or M at 1.87 hrs/ticket are closest to the L1 band) to cover L1 queues during sick days, holidays, and turnover. Even one week without coverage at this tier creates a backlog that takes weeks to clear.
Resources A, B, and C likely skew the overall average upward because their project hours are mixed in with service desk time. If Autotask uses different ticket categories for projects vs service requests, filter this report to service-only tickets. You will get a cleaner picture of your actual service desk efficiency.
All time data comes from the BI_Autotask_Time_Entries table in Proxuma Power BI. This table syncs time entries from Autotask, including hours worked, billable/non-billable split, resource name, and the linked ticket ID. The AI queries this table via DAX to calculate per-resource averages.
A resource with 3 tickets and 10 total hours would show an average of 3.33 hrs/ticket. That number is meaningless with such a small sample. The 50-ticket threshold ensures every average in this report is backed by enough data to be directionally useful. You can lower or raise this threshold in the DAX query.
Industry benchmarks vary by tier. L1 technicians handling password resets, basic troubleshooting, and software installs typically average 0.5-1.5 hours per ticket. L2 technicians on escalated issues average 2-4 hours. Anything above 4 hours per ticket usually indicates project work, complex infrastructure tickets, or a process problem.
Yes. The hours_worked field in the time entries table includes both billable and non-billable time. You can filter to billable-only by using the Billable Hours column instead. The DAX toggle in section 5.0 shows a query that splits billable and non-billable hours per resource.
Yes. Add a date filter to the DAX query using the time entry date column, or filter by ticket category to separate service requests from projects. For a QBR, filtering to the last quarter gives a more actionable picture than an all-time average.
Yes. Connect Proxuma Power BI to your Autotask 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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