This report provides a detailed breakdown of configuration item hours analysis 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("TotalCIs", COUNTROWS('BI_Autotask_Configuration_Items'), "ActiveCIs", CALCULATE(COUNTROWS('BI_Autotask_Configuration_Items'), 'BI_Autotask_Configuration_Items'[is_active] = TRUE()), "TypeCount", DISTINCTCOUNT('BI_Autotask_Configuration_Items'[configuration_item_type_name]))Estimated hour distribution by device category, derived from CI-linked ticket proportions. Hours per unit shows average maintenance burden per individual device.
| CI Type | Devices | Est. Hours | % of Total | Hrs / Unit | Burden |
|---|---|---|---|---|---|
| Server | 47 | 4,820 | 14.5% | 102.6 | High |
| Laptop - Windows | 312 | 5,110 | 15.4% | 16.4 | Medium |
| Firewall | 38 | 3,240 | 9.7% | 85.3 | High |
| Desktop - Windows | 198 | 3,890 | 11.7% | 19.6 | Medium |
| Switch | 52 | 2,180 | 6.6% | 41.9 | Medium |
| Router/Modem | 44 | 1,950 | 5.9% | 44.3 | Medium |
| Access Point | 61 | 1,640 | 4.9% | 26.9 | Normal |
| Printer | 87 | 1,180 | 3.5% | 13.6 | Normal |
| Mobile Device | 124 | 980 | 2.9% | 7.9 | Low |
| Other (14 types) | various | 7,281 | 21.9% | — | Mixed |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
FILTER(
CROSSJOIN('ConfigurationItems', 'Tickets'),
'Tickets'[ConfigurationItemID] = 'ConfigurationItems'[ID]
),
'ConfigurationItems'[Type],
"Device Count",
DISTINCTCOUNT('ConfigurationItems'[ID]),
"Total Hours",
CALCULATE(SUM('TimeEntries'[HoursWorked])),
"Ticket Count",
COUNTROWS('Tickets')
),
"Hours Per Unit",
DIVIDE([Total Hours], [Device Count])
)
ORDER BY [Total Hours] DESCEstimated hours per CI type. Servers carry the highest per-unit burden at 102.6 hours each, nearly double the next highest category.
Bar width proportional to estimated hours. Red indicates high per-unit burden; amber indicates medium burden relative to device count.
EVALUATE
TOPN(
8,
SUMMARIZE(
FILTER('Tickets', NOT ISBLANK('Tickets'[ConfigurationItemID])),
RELATED('ConfigurationItems'[Type]),
"Total Hours", CALCULATE(SUM('TimeEntries'[HoursWorked])),
"Device Count", CALCULATE(DISTINCTCOUNT('Tickets'[ConfigurationItemID]))
),
[Total Hours], DESC
)Servers consume 102.6 hours per unit on average, the highest burden of any category. With only 47 servers in the estate, this level of engagement suggests a combination of aging hardware, complex configurations, and reactive rather than proactive maintenance. The investment case for server refresh or migration to Azure AVD becomes clearer when the hour cost is this visible.
Firewalls are the second most expensive asset per unit at 85.3 hours each. At that level, every firewall in the estate carries roughly two full working weeks of engineer time annually. That is a number worth surfacing in client review meetings, particularly when vendors offer managed firewall services that shift this burden off your team.
Laptops and desktops together account for 9,000 hours, or 27% of the total, but their per-unit rate is far more reasonable at 16-20 hours per device. The sheer volume of endpoints drives the aggregate, but the per-unit story is actually quite efficient. Standard patching cadences and the use of RMM automation are likely keeping these numbers from climbing further.
The 18.7% CI linkage rate across all tickets is the most important operational signal in this dataset. More than 80% of tickets have no configuration item attached. That means the majority of hours cannot be traced back to a specific asset, which makes it nearly impossible to do true asset lifecycle costing. Improving CI linking through ticket templates and technician training could transform this dataset within a few months.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
'Tickets',
'Tickets'[AssignedTechnicianID],
"Tickets With CI",
CALCULATE(COUNTROWS('Tickets'), NOT ISBLANK('Tickets'[ConfigurationItemID])),
"Tickets Without CI",
CALCULATE(COUNTROWS('Tickets'), ISBLANK('Tickets'[ConfigurationItemID])),
"Total Tickets",
COUNTROWS('Tickets')
),
"CI Linkage Rate",
DIVIDE([Tickets With CI], [Total Tickets])
)
ORDER BY [CI Linkage Rate] DESCCI linkage depends on technicians selecting the related device when creating or resolving a ticket. In most PSA environments this field is optional, and technicians under time pressure tend to skip it. The 18.7% figure is actually above average for MSPs that haven't made CI linking a formal requirement. The practical fix is ticket templates that prompt for CI selection by default, combined with a brief period of manager review to reinforce the habit.
Yes, that level of detail is possible in Power BI when the ticket-to-CI relationship exists in the data model. This report shows type-level aggregates because the demo dataset uses proportional estimates, but with your live Autotask connection you can filter to a specific client, device type, and then down to individual CI names or serial numbers. You can even calculate the total cost of support for a single asset over its lifetime.
Take the hours per unit figure for the device type in question and multiply it by your blended engineer cost rate. A server at 102.6 hours per year at $95/hour costs roughly $9,750 in support labour annually. Compare that to the cost of a new server or a migration to a cloud alternative. The numbers often make the case on their own without any persuasion needed. This analysis is particularly effective with clients who have deferred hardware decisions for several years.
This specific report focuses on hour consumption by CI type. If your Autotask CMDB or RMM tool captures device age, purchase date, or warranty expiry, those fields can be joined to the same dataset to add a lifecycle dimension. The most useful view combines hours-per-unit with device age, which typically shows a clear correlation between older devices and higher support consumption. That combined view is a separate report in the Proxuma library.
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