This report provides a detailed breakdown of resource utilization comparison across the team for managed service providers.
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: Operations managers, service delivery leads, and MSP owners managing capacity
How often: Weekly for scheduling, monthly for utilization reviews, quarterly for staffing decisions
EVALUATE ROW(
"Total Hours", [Total],
"Billable Hours", [Billable],
"Team Billable%", [Billable % (vs Logged)],
"Total Capacity", [Capacity Total (Autotask)],
"Utilization", [Capacity Utilization Rate (Autotask)]
)The top 25 technicians by logged hours account for the bulk of all activity. The spread between rank 1 and rank 25 is more than 1,450 hours, pointing to a meaningful difference in how much time different parts of the team are recording against client work. Note that this is total logged hours, not a reflection of contracted capacity, so some variation is expected across full-time and part-time resources.
| Resource | Hours | Billable Rate |
|---|---|---|
| Dr. Amber Ayala DVM | 2,400 | 72.9% |
| James Li | 2,136 | 61.0% |
| Kevin Allen | 2,060 | 55.6% |
| Maxwell Reed | 2,050 | 89.6% |
| Andrew Roberts | 1,888 | 80.9% |
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]), "BillableRate", DIVIDE(SUM('BI_Autotask_Time_Entries'[Billable Hours]), SUM('BI_Autotask_Time_Entries'[hours_worked]))), [TotalHours], DESC)High hours and high billable % together produce the most revenue per resource. Maxwell Reed stands out here: logging 2,050 hours at 89.6% billable means 1,838 billable hours, the highest on the team. Tracy Fitzpatrick and Brandon Bishop log fewer total hours but convert almost everything they touch into billable work.
The most expensive utilization pattern in any MSP is a technician who logs many hours but converts little of it into billable work. These resources create cost without proportionate revenue. Across the team, five technicians fall into this category: high total hours combined with billable rates well below the 75.6% team average.
The most valuable combination for revenue generation is consistent hours paired with a high billable rate. These technicians are contributing disproportionately to revenue per hour logged. They are worth studying for workflow habits that could be transferred to lower-performing peers.
The top resource logs 2,400 hours while the 25th resource logs 932. When the same client base is served by a team with this range of activity, some technicians are carrying far more than their share. This risks burnout for the high-volume resources and disengagement for those at the lower end. A regular workload review across dispatchers and schedulers would help rebalance the distribution.
Logging 992 hours while generating only 107 billable hours is not typical of a front-line technical role. Either the resource is categorized incorrectly in Autotask, or time is being logged against internal work types without proper classification. This is worth one targeted conversation with the resource or their team lead before drawing broader conclusions.
These three technicians demonstrate what the top of the utilization curve looks like: high hours logged paired with 89–97% billable rates. Their work patterns, ticket categories, and time-entry habits are worth reviewing as a model. Whether through coaching, process documentation, or a peer review session, there is something transferable here for the rest of the team.
Between them, Kevin Allen and James Li logged more than 1,700 hours, but roughly half of that time did not convert to billable work. At typical MSP rates, that represents a meaningful revenue gap. A targeted conversation about time categorization — internal work being logged against client contracts, admin tasks going uncoded, or recurring work that should be blocked on retainer — could recover a significant portion of that time.
In this report, resource utilization refers to the total hours each technician has logged in Autotask, compared to the team average. It does not use scheduled capacity data because capacity records in Autotask span different time horizons for different resources. The hours-logged comparison gives a fair, data-driven view of who is recording the most activity against client and internal work.
The average includes all 68 active resources in the dataset, not just the high-volume techs shown in this top-25 view. When lower-volume resources, part-time roles, or resources who primarily handle admin work are included, the team-wide average drops. The top-25 leaderboard here represents the most active portion of the team by hours logged.
Not necessarily. Some technicians work part-time, handle escalations only, or have roles that are primarily internal. The value of this report is not prescribing a single hours target, but identifying outliers at both ends. A tech logging 2,400 hours while a colleague with the same contract logs 800 hours is a scheduling conversation worth having.
Start with the top 5 by hours and check whether their queues are being dispatched to them by default. If a dispatcher is routing all complex tickets to the same three people, the imbalance is a process issue rather than a capacity one. For the lower-volume resources, check whether they have tickets in progress or whether there are barriers in the queue preventing dispatch to them.
Yes. The same DAX query can be wrapped in a CALCULATETABLE with DATESINPERIOD to limit the time range, or filtered on the company_name column in BI_Autotask_Time_Entries to show hours per tech for a specific client. The live Power BI dashboard linked at the top of this page includes both slicers out of the box.
The numbers in this report come directly from Autotask time entries via the Proxuma Power BI connector. The AI asked one question, ran four DAX queries, and built this report in under two minutes. No manual export, no pivot tables. The same analysis runs against your live data the moment you connect.
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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