Which engineers are fast, which take longer, and whether the gap comes from ticket complexity or working speed. Generated by AI via Proxuma Power BI MCP server.
Which engineers are fast, which take longer, and whether the gap comes from ticket complexity or working speed. 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
Which engineers are fast, which take longer, and whether the gap comes from ticket complexity or working speed. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"GlobalAvgHoursPerEntry", DIVIDE(
SUM(BI_Autotask_Time_Entries[hours_worked]),
COUNTROWS(BI_Autotask_Time_Entries)),
"TotalResources", DISTINCTCOUNT(
BI_Autotask_Time_Entries[resource_name]),
"TotalTicketEntries", COUNTROWS(
FILTER(BI_Autotask_Time_Entries,
BI_Autotask_Time_Entries[ticket_id] <> BLANK()))
)Top 10 resources by total hours logged, showing ticket time, entry count, average hours per entry, and billable percentage
| # | Resource | Total Hours | Ticket Hours | Entries | Avg h/Entry | Billable % | Flag |
|---|---|---|---|---|---|---|---|
| 1 | Dr. Jessica Adams DVM | 2,400 | 1,762 | 1,731 | 1.02 | 72.9% | Above avg |
| 2 | Sarah Martinez | 2,136 | 691 | 1,781 | 0.39 | 61.0% | Normal |
| 3 | David Chen | 2,060 | 213 | 155 | 1.37 | 55.6% | Project-heavy |
| 4 | API Integration | 2,050 | 1,839 | 4,318 | 0.43 | 89.6% | Normal |
| 5 | Michael Brown | 1,888 | 1,742 | 3,623 | 0.48 | 80.9% | Normal |
| 6 | James Wilson | 1,862 | 227 | 163 | 1.39 | 76.0% | Project-heavy |
| 7 | Robert Thomas | 1,780 | 616 | 298 | 2.07 | 65.0% | 4x median |
| 8 | Emily Davis | 1,585 | 1,421 | 1,180 | 1.20 | 77.5% | Above avg |
| 9 | Lisa Anderson | 1,554 | 1,244 | 737 | 1.69 | 52.7% | 3x median |
| 10 | Gregory Horn | 1,505 | 968 | 2,952 | 0.33 | 63.6% | Fastest |
EVALUATE
ROW(
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"TotalHoursLogged", [Total],
"AvgHoursPerTicket", DIVIDE([Total], COUNTROWS('BI_Autotask_Tickets'))
)Horizontal bar chart showing average time per entry for each resource, sorted from slowest to fastest
EVALUATE
TOPN(
15,
FILTER(
ADDCOLUMNS(
SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"LoggedHours", [Total],
"TicketsTouched", CALCULATE(DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id]), 'BI_Autotask_Time_Entries'[ticket_id] <> BLANK())
),
"HoursPerTicket", DIVIDE([LoggedHours], [TicketsTouched])
),
[TicketsTouched] >= 20
),
[HoursPerTicket], DESC
)
ORDER BY [HoursPerTicket] DESCResources with low ticket entry counts but high averages often spend most of their time on project work, which naturally takes longer per entry
| Resource | Total Hours | Ticket Hours | Ticket % | Project Hours | Project % | Profile |
|---|---|---|---|---|---|---|
| David Chen | 2,060 | 213 | 10.3% | 1,847 | 89.7% | Project specialist |
| James Wilson | 1,862 | 227 | 12.2% | 1,635 | 87.8% | Project specialist |
| Robert Thomas | 1,780 | 616 | 34.6% | 1,164 | 65.4% | Mixed — investigate |
| Lisa Anderson | 1,554 | 1,244 | 80.1% | 310 | 19.9% | Ticket — slow avg |
| Michael Brown | 1,888 | 1,742 | 92.3% | 146 | 7.7% | Ticket specialist |
| Gregory Horn | 1,505 | 968 | 64.3% | 537 | 35.7% | Balanced — fast |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Time_Entries,
BI_Autotask_Time_Entries[resource_name]
),
"TotalHours", CALCULATE(
SUM(BI_Autotask_Time_Entries[hours_worked])),
"TicketHours", CALCULATE(
SUM(BI_Autotask_Time_Entries[hours_worked]),
BI_Autotask_Time_Entries[ticket_id] <> BLANK()),
"ProjectHours", CALCULATE(
SUM(BI_Autotask_Time_Entries[hours_worked]),
BI_Autotask_Time_Entries[ticket_id] = BLANK()),
"TicketPct", DIVIDE(
CALCULATE(SUM(BI_Autotask_Time_Entries[hours_worked]),
BI_Autotask_Time_Entries[ticket_id] <> BLANK()),
CALCULATE(SUM(BI_Autotask_Time_Entries[hours_worked])))
)
ORDER BY [TotalHours] DESCBillable percentage shows how much of each engineer's logged time translates into revenue. Low billable rates on high-volume ticket workers are a margin leak.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Time_Entries,
BI_Autotask_Time_Entries[resource_name]
),
"TotalHours", CALCULATE(
SUM(BI_Autotask_Time_Entries[hours_worked])),
"BillableHours", CALCULATE(
SUM(BI_Autotask_Time_Entries[Billable Hours])),
"BillablePct", DIVIDE(
CALCULATE(SUM(BI_Autotask_Time_Entries[Billable Hours])),
CALCULATE(SUM(BI_Autotask_Time_Entries[hours_worked])))
)
ORDER BY [BillablePct] DESCThe global average of 0.49 hours per ticket entry (about 30 minutes) is a reasonable baseline for an MSP handling a mix of password resets, workstation issues, and escalated problems. But the spread across resources is wide: from Gregory Horn at 0.33 hours to Robert Thomas at 2.07 hours. That 6x gap is where the real story is.
Robert Thomas is the biggest outlier. At 2.07 hours per ticket entry across 298 entries, he averages more than two hours on every ticket he touches. His profile is mixed: 34.6% ticket work, 65.4% project work, with a billable rate of 65.0%. He is not a pure project engineer. The high average on ticket work specifically suggests either he handles only escalated or complex tickets, or there is an efficiency gap worth investigating.
Lisa Anderson shows a similar pattern but with more volume. She has 737 ticket entries at 1.69 hours each, and 80.1% of her time goes to tickets. That rules out the "mostly project work" explanation. Her billable rate of 52.7% is the lowest in the top 10. Between the slow ticket time and low billable rate, she represents the largest potential efficiency gain in the group.
David Chen and James Wilson both log over 1.3 hours per ticket entry, but their profiles explain why. Both spend less than 13% of their time on tickets and over 87% on projects. Their ticket entries are likely setup tasks or escalation responses tied to project work. High averages are expected for that type of work.
On the efficient end, Gregory Horn at 0.33 hours and Sarah Martinez at 0.39 hours handle high volumes at speed. Horn processed 2,952 ticket entries at 20 minutes each. Martinez handled 1,781 entries at under 24 minutes each. These two should be studied for their workflow patterns: what tools they use, how they triage, and whether their ticket queues are naturally simpler or they are genuinely faster.
API Integration at 0.43 hours across 4,318 entries is an automated or semi-automated resource. Its 89.6% billable rate and massive volume make it the most efficient "resource" in the dataset, which makes sense for system-generated time entries.
5 priorities based on the findings above
At 2.07 hours per entry, Robert spends 4x the median on every ticket. Pull his recent ticket entries and check: is he assigned complex escalation tickets, or is he spending two hours on routine issues? If it is the former, his queue assignment is fine and his average is expected. If it is the latter, he needs coaching, better templates, or a workflow review. 298 entries at 2.07 hours each means roughly 300 hours of potential savings if brought to the median.
Lisa has the lowest billable rate (52.7%) and the second-highest average time per ticket (1.69h) among primarily ticket-focused resources. She handles 737 ticket entries, so this is not a small-sample problem. Check whether she is logging internal time correctly, or whether she is working on tickets that should be categorized differently. A 10% improvement in her billable rate would recover over 155 billable hours per year.
Horn and Martinez consistently handle tickets in under 25 minutes at high volume. Shadow sessions or shared screen recordings of their triage and resolution workflows would give slower resources a concrete model to follow. This is cheaper than tooling changes and often more effective than generic training.
David Chen and James Wilson are project specialists who happen to log a few ticket entries. Including them in ticket-efficiency rankings inflates the "slow" end of the distribution and makes the data harder to read. Create a filtered view that separates resources with more than 70% ticket time from those with less. This gives you a cleaner comparison for ticket-focused engineers.
Some of the variance in average time per ticket comes from how engineers log their work. If one engineer logs a single 2-hour entry for a complex ticket while another logs four 30-minute entries for the same work, their averages will look very different. Set a team standard: one time entry per meaningful work block, with a maximum of 1 hour per entry before splitting. This makes the data comparable and the outliers real.
All time data comes from Autotask PSA time entries. When an engineer logs time against a ticket, Autotask records the resource name, hours worked, ticket ID, and whether the time is billable. Proxuma Power BI pulls these entries through the Autotask connector and makes them available for DAX queries. The AI then calculates averages, groups by resource, and formats the results.
It divides the total ticket hours for a resource by the number of ticket time entries they logged. If an engineer logged 500 hours across 1,000 ticket entries, their average is 0.50 hours (30 minutes) per entry. This measures time per work session, not per unique ticket. A single ticket can have multiple time entries from the same or different engineers.
Autotask can create automated time entries through API integrations (such as RMM tools or automation platforms). These entries are logged under a system resource name. Including them shows the volume and efficiency of automated work compared to human engineers. You can filter them out in your own Power BI instance if you want a human-only view.
It depends on your ticket mix. MSPs that handle mostly password resets and basic workstation issues typically see 15 to 30 minutes per entry. MSPs with a heavier mix of network, server, and security tickets see 30 to 60 minutes. Anything consistently above 1 hour per entry on standard service desk tickets is worth investigating, but context matters more than a single benchmark.
Yes. The DAX queries in this report use the full time entries table without queue or category filters. You can add filters on ticket queue, priority, or category in your Power BI instance to compare engineers within the same type of work. That gives a more fair comparison than the raw average, which mixes simple and complex tickets together.
Yes. Connect Proxuma Power BI to your Autotask PSA 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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