Generated by AI via Proxuma Power BI MCP server. Per-technician efficiency analysis: ticket volume, hours worked, billable hours, and average time per ticket.
Generated by AI via Proxuma Power BI MCP server. Per-technician efficiency analysis: ticket volume, hours worked, billable hours, and average time 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
Generated by AI via Proxuma Power BI MCP server. Per-technician efficiency analysis: ticket volume, hours worked, billable hours, and average time per ticket.
EVALUATE
ROW(
"Total_Tickets", COUNTROWS('BI_Autotask_Tickets'),
"Total_Hours", SUM('BI_Autotask_Tickets'[worked_hours]),
"Total_Billable", SUM('BI_Autotask_Tickets'[billable_hours]),
"Avg_Hours_Per_Ticket",
DIVIDE(
SUM('BI_Autotask_Tickets'[worked_hours]),
COUNTROWS('BI_Autotask_Tickets')
)
)Excludes system/automation accounts. Sorted by ticket count descending.
| Resource | Hours | Tickets | Avg h/Ticket |
|---|---|---|---|
| Shannon Farley | 19 | 127 | 0.15 |
| Mark Glenn | 134 | 606 | 0.22 |
| Samantha Ibarra | 9 | 34 | 0.26 |
| 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 |
| Sheila Morales | 185 | 425 | 0.43 |
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)The data splits naturally into two groups. High-volume, low-hours-per-ticket engineers like Tracy Fitzpatrick (3,600 tickets, 0.31 hrs each) and Gregory Horn (3,240 tickets, 0.32 hrs each) are resolving tickets fast. Their billable hours are healthy, and the numbers suggest they are handling straightforward T&M work at pace. These engineers are your service desk backbone.
The second group has fewer tickets but significantly more time per ticket. Maxwell Reed (1,906 tickets, 0.81 hrs) and Andrew Roberts (1,899 tickets, 0.92 hrs) are spending nearly twice as long per ticket as the high-volume group. This is not necessarily inefficiency. Higher time per ticket often correlates with higher billable recovery. Andrew Roberts generated 1,919 billable hours against 1,747 worked, a billable rate above 100%, which suggests accurate time logging on complex paid work.
Two engineers stand out for different reasons. Jane Stewart and David Collins both handle high volumes at very low hours-per-ticket (0.16 and 0.26), but their billable hours are minimal. This is consistent with a triage or dispatcher role, not hands-on technical work. Stephen Nelson is the biggest concern: 1,336 tickets, 423 hours logged, but essentially zero billable hours. This needs investigation to understand whether it is a contract type, role classification, or a billing data issue.
4 actions based on the data above
Tracy Fitzpatrick, Gregory Horn, and Brandon Bishop together handle over 9,000 tickets at a combined 0.35 average hours per ticket. Their billable hours are solid. These engineers are the foundation of your service desk capacity. Make sure their workload stays balanced and they are not absorbing tickets that should go to specialist engineers.
Andrew Roberts spent 1,747 hours on 1,899 tickets and generated 1,919 billable hours. That is excellent utilization. When average time per ticket is high, look at the billable hours alongside it before drawing conclusions. The goal is high billable recovery, not minimum time spent.
This engineer logged 423 hours across 1,336 tickets and generated almost no billable time. Three possible explanations: all work is on non-billable managed contracts, the role is internal or administrative, or time entries are not being flagged correctly. Pull the ticket detail for this engineer and check the work type and billing category distributions immediately.
Jane Stewart and David Collins both show patterns consistent with triage or dispatcher roles: high ticket count, low hours, minimal billing. If they are in hybrid roles (triage and technical work), their metrics are misleading when averaged. Consider segmenting your efficiency reports by role type rather than treating all engineers the same.
No. Lower time per ticket is only better when the resolution quality is maintained and billing isn’t being under-captured. An engineer who resolves tickets quickly and bills accurately is ideal. An engineer who resolves tickets quickly by deflecting or closing without resolution is not. Always cross-reference time per ticket with ticket reopen rates and client CSAT scores.
In Autotask, worked hours and billable hours are separate fields. Billable hours can exceed worked hours if the billing rate differs from the actual time logged, or if billing adjustments have been made. It is worth investigating but is not necessarily an error.
Yes. You can add filters to the DAX queries to narrow down by work type, priority, client, or date range. Ask the AI to modify the query with the specific filter you need. For example: “Show me average time per ticket per tech for P1 and P2 tickets only.”
Yes. Connect Proxuma Power BI to your Autotask PSA account, add an AI tool 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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