A full breakdown of hours worked, billable split, and hours billed across your top 10 resources from Autotask PSA time entries. See who logs the most, who bills the highest ratio, and where non-billable time is piling up. PSA
A full breakdown of hours worked, billable split, and hours billed across your top 10 resources from Autotask PSA time entries. See who logs the most, who bills the highest ratio, and where non-billable time is piling up. PSA
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
A full breakdown of hours worked, billable split, and hours billed across your top 10 resources from Autotask PSA time entries. See who logs the most, who bills the highest ratio, and where non-billable time is piling up. PSA
Company-level time entry metrics across all resources in the Autotask PSA dataset.
EVALUATE
ROW(
"TotalWorked", [Company - Hours Worked],
"TotalBillable", [Company - Billable Hours],
"TotalBilled", [Company - Hours Billed]
)Top 10 resources ranked by total hours worked. The segmented bar chart shows the billable vs non-billable split for each resource.
| Resource | Total Hours | Billable | Tickets |
|---|---|---|---|
| Dr. Amber Ayala DVM | 2,400 | 1,749 (72.9%) | 603 |
| James Li | 2,136 | 1,303 (61.0%) | 794 |
| Kevin Allen | 2,060 | 1,145 (55.6%) | 99 |
| Maxwell Reed | 2,050 | 1,838 (89.6%) | 2,613 |
| Andrew Roberts | 1,888 | 1,527 (80.9%) | 2,297 |
| David Hunt | 1,862 | 1,416 (76.0%) | 84 |
| Chelsea Thomas | 1,780 | 1,157 (65.0%) | 149 |
| Jennifer King | 1,585 | 1,228 (77.5%) | 763 |
| Jerry Mcfarland | 1,554 | 819 (52.7%) | 489 |
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]), "BillableHours", SUM('BI_Autotask_Time_Entries'[Billable Hours]), "NonBillable", SUM('BI_Autotask_Time_Entries'[Non billable Hours]), "TicketCount", DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id])), [TotalHours], DESC)Resources ranked by billable percentage. The higher the ratio, the more of their logged time translates to revenue. Industry benchmark for MSPs is typically 65-75%.
EVALUATE
TOPN(10,
ADDCOLUMNS(
SUMMARIZE('BI_Autotask_Time_Entries', 'BI_Autotask_Time_Entries'[resource_name]),
"BillableRate", DIVIDE(SUM('BI_Autotask_Time_Entries'[Billable Hours]), SUM('BI_Autotask_Time_Entries'[hours_worked]), 0)
),
[BillableRate], DESC
)With 1,838 billable hours out of 2,050 total, Resource D operates at an 89.7% billable rate. That is 20 points above the team average and well above industry benchmarks. Only 213 hours went to non-billable work. This resource is either highly specialized in client-facing work or has minimal internal overhead. Either way, it sets the bar for what is achievable.
Resource I logs only 52.7% billable time (819 out of 1,554 hours), and Resource C sits at 55.6% (1,145 out of 2,060 hours). Combined, these two resources account for 1,650 non-billable hours. That is nearly 30% of all non-billable time across the top 10. Investigate whether these resources carry internal project load, training duties, or simply have time entry classification issues.
Across 8 of 10 resources, hours billed is higher than hours worked. The total gap is 637 hours (19,457 billed vs 18,820 worked). This is common in fixed-fee or block-hour billing models where the billed amount reflects the contract value rather than actual time spent. Still worth reviewing: if actual time exceeds billed time for a resource, that is margin leakage. If billed time consistently exceeds worked time by wide margins, contracts might be overpriced relative to effort.
The data tells a clear story: your team collectively operates at a 69.8% billable rate, which puts you in the acceptable range for MSPs but leaves room for improvement. The spread between your most efficient resource (89.7%) and your least efficient (52.7%) is 37 percentage points. That gap signals inconsistent workload distribution or role-based differences that are not reflected in how time is categorized.
Start with Resource I and Resource C. Together they represent 3,614 hours worked but only 1,964 billable hours. Before assuming these resources are underperforming, check whether they handle internal projects, documentation, or training. If their non-billable time is legitimate operational work, consider creating separate time categories so it does not drag down the billable metric. If it is genuinely lost productivity, that is a scheduling or utilization problem.
Standardize how "hours billed" maps to "hours worked." The fact that billed hours exceed worked hours for most resources is not a problem on its own, but it makes apples-to-apples comparison harder. If your billing model is fixed-fee, track "effective hourly rate" (revenue / hours worked) as a companion metric. This gives a clearer picture of which resources generate the most value per hour spent.
Set a team target of 72-75% billable rate. That is realistic given that Resource D already hits 89.7% and five others sit above 72%. The three resources below 65% are the ones pulling the average down. A 5-point improvement across those three would push the team average above 72% and recover roughly 400 billable hours per year.
Hours worked is the total time a resource logged in Autotask time entries, regardless of billing status. Hours billed is the amount that appears on invoices or is counted toward contract fulfillment. In fixed-fee or block-hour agreements, these numbers often differ because billing is based on contract terms rather than actual time spent.
Most MSP benchmarks place a healthy billable rate between 65% and 75% for technical resources. Below 60% usually indicates too much internal overhead or poor time entry discipline. Above 80% is excellent but can be hard to sustain without burning out staff or neglecting internal projects.
Yes. Copy any query from the toggles above and paste it into DAX Studio or the Power BI Desktop performance analyzer. The queries reference standard Proxuma data model tables and measures that exist in every Proxuma Power BI deployment. Resource names in your dataset will appear instead of the anonymized labels used here.
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