“Time Distribution by Work Type”
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Time Distribution by Work Type

A breakdown of 50,752 hours across ticket work, project tasks, internal overhead, and recurring service calls, showing where your MSP spends its time and how much of it is billable.

Built from: Autotask PSA
How this report was made
1
Autotask PSA
Multiple data sources combined
2
Proxuma Power BI
Pre-built MSP semantic model, 50+ measures
3
AI via MCP
Claude or ChatGPT writes DAX queries, executes them, formats output
4
This Report
KPIs, breakdowns, trends, recommendations
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Time Distribution by Work Type

A breakdown of 50,752 hours across ticket work, project tasks, internal overhead, and recurring service calls, showing where your MSP spends its time and how much of it is billable.

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

Time saved
Manual data extraction and formatting takes hours. This report delivers results in minutes.
Operational clarity
Key metrics and breakdowns that would otherwise require custom queries.
Decision support
Data-driven evidence for operational decisions and process improvements.
Report categoryOther
Data sourceAutotask PSA · Datto RMM · Datto Backup · Microsoft 365 · SmileBack · HubSpot · IT Glue
RefreshReal-time via Power BI
Generation timeUnder 15 minutes
AI requiredClaude, ChatGPT or Copilot
AudienceMSP operations teams
Where to find this in Proxuma
Power BI › Report › Time Distribution by Work Type
What you can measure in this report
Key Metrics
Hours by Work Type
Billable vs. Non-Billable Split
Top Resources by Work Type Distribution
Non-Billable Hours Breakdown
Key Findings
Frequently Asked Questions
Total Hours
Billable Hours
Non-Billable
Dominant Type
Power BI · AI-Generated Report
Data source: Autotask PSA
Date: March 2026
Scope: All time entries
Sources: Autotask PSA

Time Distribution by Work Type

A breakdown of 50,752 hours across ticket work, project tasks, internal overhead, and recurring service calls, showing where your MSP spends its time and how much of it is billable.

1.0 Key Metrics
Total Hours
50,752
82,790 time entries
Billable Hours
38,364
75.6% of total
Non-Billable
12,388
24.4% of total
Dominant Type
Tickets
65.5% of all hours
View DAX Query — Key metrics summary 
EVALUATE
SUMMARIZE(
    BI_Autotask_Time_Entries,
    BI_Autotask_Time_Entries[time_entry_type],
    "Entries", COUNTROWS(BI_Autotask_Time_Entries),
    "Hours", SUM(BI_Autotask_Time_Entries[hours_worked]),
    "Billable", SUM(BI_Autotask_Time_Entries[hours_to_bill])
)
2.0 Hours by Work Type

Breakdown of all 50,752 hours across the four work type categories logged in Autotask.

Work TypeType IDEntriesHours% of TotalBillableNon-BillableBillable Rate
Ticket WorkType 274,13833,27165.6%29,2544,01787.9%
Project Task WorkType 64,66210,21620.1%9,1091,10789.2%
Internal / Non-BillableType 103,5835,40010.6%05,4000.0%
Service Call / RecurringType 174071,8643.7%01,8640.0%
Ticket Work
33,271h
65.5%
Project Tasks
10,216h
20.1%
Internal
5,400h
10.6%
Service Calls
3.7%
View DAX Query — Hours by work type 
EVALUATE ADDCOLUMNS(SUMMARIZE('BI_Autotask_Time_Entries','BI_Autotask_Time_Entries'[time_entry_type]), "Entries", CALCULATE(COUNTROWS('BI_Autotask_Time_Entries')), "Hours", CALCULATE(SUM('BI_Autotask_Time_Entries'[hours_worked])), "BillableHours", CALCULATE(SUM('BI_Autotask_Time_Entries'[Billable Hours])), "NonBillableHours", CALCULATE(SUM('BI_Autotask_Time_Entries'[Non billable Hours]))) ORDER BY [Hours] DESC
3.0 Billable vs. Non-Billable Split

How each work type contributes to the overall billable and non-billable totals.

75.6% billable Overall Billable Rate
87.9% Ticket Work
89.2% Project Tasks
0% Internal + Service
Billable breakdown: Ticket work contributes 29,254h billable (76.2% of all billable hours). Project tasks add 9,109h (23.8%). Internal and service call work types contribute zero billable hours by definition. The 4,017h of non-billable ticket time is worth investigating: that is time spent on tickets that was not charged to the client.
View DAX Query — Billable split per work type 
EVALUATE
ADDCOLUMNS(
    SUMMARIZE(
        BI_Autotask_Time_Entries,
        BI_Autotask_Time_Entries[time_entry_type]
    ),
    "TotalHours", SUM(BI_Autotask_Time_Entries[hours_worked]),
    "BillableHours", SUM(BI_Autotask_Time_Entries[hours_to_bill]),
    "NonBillable", SUM(BI_Autotask_Time_Entries[hours_worked])
        - SUM(BI_Autotask_Time_Entries[hours_to_bill]),
    "BillableRate", DIVIDE(
        SUM(BI_Autotask_Time_Entries[hours_to_bill]),
        SUM(BI_Autotask_Time_Entries[hours_worked])
    )
)
ORDER BY [TotalHours] DESC
4.0 Top Resources by Work Type Distribution

How the top five resources split their time across ticket work, project tasks, and internal hours.

Resource Ticket Hours Project Hours Internal Hours Total Profile
API Integration 1,839 7 205 2,051 Automated
Dr. Jessica Adams 1,762 330 119 2,211 Ticket-heavy
Michael Brown 1,742 21 99 1,862 Ticket-heavy
David Chen 213 1,104 395 1,712 Project specialist
Sarah Martinez 691 693 586 1,970 Balanced / high internal
API Integration
90%
10%
Dr. J. Adams
80%
15%
M. Brown
94%
D. Chen
12%
65%
23%
S. Martinez
35%
35%
30%
Ticket Work Project Tasks Internal
View DAX Query — Resource distribution by work type 
EVALUATE
ADDCOLUMNS(
    SUMMARIZE(
        BI_Autotask_Time_Entries,
        BI_Autotask_Time_Entries[resource_name]
    ),
    "TicketHours", CALCULATE(
        SUM(BI_Autotask_Time_Entries[hours_worked]),
        BI_Autotask_Time_Entries[time_entry_type] = 2
    ),
    "ProjectHours", CALCULATE(
        SUM(BI_Autotask_Time_Entries[hours_worked]),
        BI_Autotask_Time_Entries[time_entry_type] = 6
    ),
    "InternalHours", CALCULATE(
        SUM(BI_Autotask_Time_Entries[hours_worked]),
        BI_Autotask_Time_Entries[time_entry_type] = 10
    )
)
ORDER BY [TicketHours] + [ProjectHours] + [InternalHours] DESC
5.0 Non-Billable Hours Breakdown

Where the 12,388 non-billable hours come from, by source category.

Internal (Type 10)
5,400h
43.6%
Ticket Non-Bill
4,017h
32.4%
Service Calls
1,864h
15.0%
Project Non-Bill
1,107h
8.9%
Key takeaway: The largest non-billable block is internal time at 5,400h, which is expected. But the second largest, 4,017h of non-billable ticket work, is time spent on client tickets that was never billed. That is 12.1% of all ticket hours. If even half of those hours could be reclassified or billed, it would add roughly 2,000 billable hours to the total.
View DAX Query — Non-billable hours by source 
EVALUATE
ADDCOLUMNS(
    SUMMARIZE(
        BI_Autotask_Time_Entries,
        BI_Autotask_Time_Entries[time_entry_type]
    ),
    "NonBillableHours",
        SUM(BI_Autotask_Time_Entries[hours_worked])
        - SUM(BI_Autotask_Time_Entries[hours_to_bill])
)
ORDER BY [NonBillableHours] DESC
6.0 Key Findings
1

Ticket work drives the business

65.5% of all hours go to ticket work, and 87.9% of those hours are billable. This is the engine. Any automation or efficiency gain in ticket handling directly improves revenue per hour.

2

4,017 hours of ticket time went unbilled

That is 12.1% of all ticket hours. Common causes: internal notes counted as work time, pre-sales troubleshooting, or tickets closed without proper billing codes. A billing review on this category alone could recover meaningful revenue.

3

Project work is highly billable at 89.2%

Project task hours have the highest billable rate across all categories. The 1,107h of non-billable project time likely covers scoping, handoff meetings, and internal project management overhead.

4

Sarah Martinez carries disproportionate internal load

With 586h of internal time (29.7% of her total), Sarah absorbs more overhead than any other resource. If that includes onboarding, documentation, or admin tasks, consider distributing that load or automating parts of it.

5

Service call hours are 100% non-billable

The 1,864h logged under Type 17 (Service Call / Recurring) generate zero billable hours. If these are recurring maintenance tasks for managed service clients, they should be factored into contract pricing. If they are ad-hoc, there may be a billing gap.

7.0 Frequently Asked Questions
What do the time entry type numbers mean?

Autotask assigns a numeric ID to each work type. Type 2 is ticket work (service desk time entries). Type 6 is project task work. Type 10 is internal or non-billable time. Type 17 covers service calls and recurring work. These IDs are set by Autotask and cannot be changed.

Why is ticket non-billable time so high?

The 4,017h of non-billable ticket time typically comes from internal notes logged on tickets, pre-sales troubleshooting, goodwill write-offs, or time entries missing a billing code. Running a separate report filtered to non-billable ticket entries can identify specific patterns.

How should I interpret the resource profiles?

A "Ticket-heavy" profile means the resource spends the vast majority of time on reactive service desk work. A "Project specialist" is mostly on project deliverables. "Balanced / high internal" means the resource splits time across categories but absorbs more internal overhead than average. These profiles help with capacity planning and role alignment.

What is the API Integration resource?

API Integration is an automated resource in Autotask. It logs time entries generated by integrations, RMM tools, or automated workflows rather than by a human technician. The 1,839h of ticket time from this resource represents automated processing, not manual labor.

Can I run this report against my own data?

Yes. Connect Proxuma Power BI to your Autotask PSA, 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.

Demo Report: This report uses synthetic data from a sample Autotask environment. Connect your own PSA to Proxuma Power BI to generate this report with your real time entries.
Proxuma Power BI · AI-Generated Report · proxuma.io/powerbi Generated in < 15 minutes via MCP · March 2026
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