How many unwritten hours does each resource have? This report compares Autotask capacity against actual logged time to expose the gap between what your team could bill and what they actually record.
How many unwritten hours does each resource have? This report compares Autotask capacity against actual logged time to expose the gap between what your team could bill and what they actually record.
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: MSP operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
How many unwritten hours does each resource have? This report compares Autotask capacity against actual logged time to expose the gap between what your team could bill and what they actually record.
High-level time entry metrics across all resources and time periods.
Top 15 resources ranked by total hours logged. These are the most active loggers, not the worst offenders.
The top 15 resources account for 25,869 hours of the 50,752 total logged hours (51%). Tech A leads with 2,400 hours, while Tech O logged 1,344. Keep in mind: these are the best loggers. The remaining 62 resources logged even fewer hours each, averaging just 401 hours per person.
EVALUATE
TOPN(15,
SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"BillableHours", CALCULATE(
SUM('BI_Autotask_Time_Entries'[hours_worked]),
'BI_Autotask_Time_Entries'[is_non_billable] = FALSE
),
"NonBillableHours", CALCULATE(
SUM('BI_Autotask_Time_Entries'[hours_worked]),
'BI_Autotask_Time_Entries'[is_non_billable] = TRUE
),
"ClientCount", DISTINCTCOUNT(
'BI_Autotask_Time_Entries'[company_id]
)
),
[TotalHours], DESC
)Translating unwritten hours into potential revenue using the average billable rate.
The math is simple but uncomfortable. Your team generates $459 per billable hour on average. With 737,106 unwritten hours in the system, even a tiny fraction of recoverable time translates to serious money. If just 1% of those hours represented real billable work that went unrecorded, that is $3.4 million in missed revenue. At 5%, it is $16.9 million. Nobody expects to capture all of it, but the gap between zero and "some" is worth millions.
EVALUATE ROW("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]), "TotalTickets", [Tickets - Count - Created], "TicketsWithTime", DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id]))Breakdown of hours logged, billable split, ticket count, and client coverage for the top 15 resources.
| Resource | Total Hrs | Billable | Non-Bill | Bill % | Tickets | Clients |
|---|---|---|---|---|---|---|
| Tech A | 2,400 | 1,749 | 651 | 72.9% | 603 | 46 |
| Tech B | 2,136 | 1,303 | 833 | 61.0% | 794 | 117 |
| Tech C | 2,060 | 1,145 | 915 | 55.6% | 99 | 54 |
| Tech D | 2,050 | 1,838 | 213 | 89.7% | 2,613 | 115 |
| Tech E | 1,888 | 1,527 | 361 | 80.9% | 2,297 | 104 |
| Tech F | 1,862 | 1,416 | 446 | 76.0% | 84 | 45 |
| Tech G | 1,780 | 1,157 | 623 | 65.0% | 149 | 44 |
| Tech H | 1,585 | 1,228 | 357 | 77.5% | 763 | 77 |
| Tech I | 1,554 | 819 | 735 | 52.7% | 489 | 29 |
| Tech J | 1,505 | 957 | 547 | 63.6% | 2,017 | 143 |
| Tech K | 1,492 | 1,094 | 399 | 73.3% | 724 | 84 |
| Tech L | 1,433 | 1,308 | 125 | 91.3% | 17 | 25 |
| Tech M | 1,418 | 1,344 | 75 | 94.8% | 3,220 | 146 |
| Tech N | 1,362 | 1,322 | 40 | 97.1% | 3,275 | 137 |
| Tech O | 1,344 | 1,087 | 257 | 80.9% | 578 | 51 |
Billable rates range from 52.7% (Tech I) to 97.1% (Tech N) among the top loggers. Tech N and Tech M stand out: they log nearly all their time as billable and handle the highest ticket volumes (3,275 and 3,220 respectively). On the other end, Tech C and Tech I have the lowest billable ratios, with over 40% of their logged time going to non-billable activities.
How the 50,752 logged hours split between billable and non-billable work.
Of the hours that do get logged, three quarters are billable. That is a healthy ratio. The real problem is not billable vs non-billable. It is logged vs unlogged. Getting technicians to log more time overall will naturally increase billable hours too, because most of the missing time is likely billable work that never made it into the system.
EVALUATE
ROW(
"BillableHours", CALCULATE(
SUM('BI_Autotask_Time_Entries'[hours_worked]),
'BI_Autotask_Time_Entries'[is_non_billable] = FALSE
),
"NonBillableHours", CALCULATE(
SUM('BI_Autotask_Time_Entries'[hours_worked]),
'BI_Autotask_Time_Entries'[is_non_billable] = TRUE
),
"TotalLoggedHours", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"BillablePercent", DIVIDE(
CALCULATE(
SUM('BI_Autotask_Time_Entries'[hours_worked]),
'BI_Autotask_Time_Entries'[is_non_billable] = FALSE
),
SUM('BI_Autotask_Time_Entries'[hours_worked])
)
)The relationship between ticket volume and hours logged reveals different working patterns.
Two distinct patterns emerge. Tech M and Tech N handle massive ticket volumes (3,000+) while logging modest hours, pointing to fast-touch, high-volume work. Tech C and Tech F log many hours against very few tickets, suggesting project-style or deep-dive work. Tech L is an outlier: 1,433 hours logged against only 17 tickets, which could indicate project work, internal tasks, or time logged against non-ticket activities.
What the data tells us about time logging behavior and revenue exposure.
Only 6.4% of Autotask capacity has time entries. Even accounting for the fact that capacity is cumulative and includes inactive periods, this gap is far too wide. If resources are working 8 hours a day but logging 1, you are blind to 7 hours of activity. That makes utilization reporting, profitability analysis, and resource planning unreliable.
At an average rate of $459 per billable hour, the revenue impact of missing time entries is staggering. A technician who forgets to log 30 minutes of billable work each day costs roughly $57,000 per year. Multiply that across 77 resources, even partially, and you are looking at millions in leaked revenue.
Tech D, E, M, and N all handle 2,000+ tickets and maintain billable rates above 80%. Ticket-driven workflows naturally produce time entries because the ticket itself is a prompt to log. Resources doing project or internal work (Tech C, Tech L) show lower billable rates. Process matters as much as discipline.
Among the best loggers, billable rates average 76.1%. That is healthy. The problem is not what gets logged. The problem is the work that never gets logged at all. Fix the logging behavior and the revenue follows.
Steps to close the gap between actual work and recorded time.
Set up a daily automated report that flags any resource with zero time entries for the previous business day. Send it to team leads by 9 AM. Make logging non-negotiable. Target: 100% of resources log at least 6 hours per working day within 60 days.
Rank resources by logging compliance and share it with the team. Positive peer pressure works. Include both total hours logged and billable percentage. Recognize the top loggers publicly. The data from section 4.0 is already the foundation for this leaderboard.
The bottom 62 resources average just 401 hours each. Some may be part-time, inactive, or administrative accounts. Clean up inactive resources from the capacity calculation, then focus compliance efforts on the active ones who are genuinely underlogging. Getting 10 of them to Tech A levels would add thousands of billable hours.
Autotask capacity is cumulative across all time periods and all resources in the system. It includes weekends, holidays, and periods when resources may not have been active. The raw number looks alarming, but the real metric to watch is logging compliance per working day per active resource.
No. The $33.8M figure is a scenario calculation showing what 10% of unwritten hours would be worth at the average billable rate. The actual revenue leak depends on how many unlogged hours represent real billable work. Even a conservative estimate of 1-2% still translates to millions.
Compare each resource's logged hours against their expected working hours for the period. Use the DAX queries in this report to pull per-resource data and calculate a compliance percentage. Resources logging less than 70% of expected hours need immediate attention.
Industry benchmarks suggest 85-95% of working hours should have time entries. Best-in-class MSPs target 95%+ with automated prompts and mandatory daily logging. Start with 85% as a minimum and work upward.
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.
Weekly for compliance tracking, monthly for trend analysis. Set up automated alerts for any resource logging less than 6 hours on a business day. The goal is to catch gaps in real time, not discover them months later.
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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