Configuration item breakdown by type, engineer workload distribution, and billable vs non-billable analysis. Generated by AI via Proxuma Power BI MCP server.
Configuration item breakdown by type, engineer workload distribution, and billable vs non-billable analysis. Generated by AI via Proxuma Power BI MCP server.
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: NOC teams, asset managers, and service delivery leads
How often: Weekly for fleet reviews, monthly for lifecycle planning, quarterly for budgeting
Configuration item breakdown by type, engineer workload distribution, and billable vs non-billable analysis. Generated by AI via Proxuma Power BI MCP server.
Configuration items grouped by type, sorted by count. Workstations dominate the asset base at 50.3% of all CIs.
Five additional CI types (Azure AVD, Dockingstation, Monitor, Conference setup, UPS) account for a combined 72 items. These long-tail categories are not shown in the chart but are included in all aggregate calculations.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Configuration_Items'[configuration_item_type_name],
"CICount", COUNTROWS('BI_Autotask_Configuration_Items')
)
ORDER BY [CICount] DESCProportion of total hours consumed by the top five CI types. Workstations generate the most volume, but servers and network devices can be disproportionately time-intensive per unit.
Workstations make up half the CMDB. That is expected for a typical MSP. The question is whether workstations also consume half the engineer hours, or whether smaller categories like servers and firewalls take a disproportionate share. Section 6.0 addresses this in detail.
Top 10 engineers ranked by total hours worked, with billable/non-billable split and ticket count
| # | Engineer | Total Hours | Billable | Non-Billable | Billable % | Tickets |
|---|---|---|---|---|---|---|
| 1 | Engineer A | 2,400 | 1,749 | 651 | 72.9% | 603 |
| 2 | Engineer B | 2,136 | 1,303 | 833 | 61.0% | 794 |
| 3 | Engineer C | 2,060 | 1,145 | 915 | 55.6% | 99 |
| 4 | Engineer D | 2,050 | 1,838 | 213 | 89.7% | 2,613 |
| 5 | Engineer E | 1,888 | 1,527 | 361 | 80.9% | 2,297 |
| 6 | Engineer F | 1,862 | 1,416 | 446 | 76.0% | 84 |
| 7 | Engineer G | 1,780 | 1,157 | 623 | 65.0% | 149 |
| 8 | Engineer H | 1,585 | 1,228 | 357 | 77.5% | 763 |
| 9 | Engineer I | 1,554 | 819 | 735 | 52.7% | 489 |
| 10 | Engineer J | 1,505 | 957 | 548 | 63.6% | 2,017 |
Engineer D stands out with the highest billable rate (89.7%) and the most tickets (2,613). That combination suggests high-volume, well-tracked billable work. On the other end, Engineer C and Engineer I sit below 56% billable, which means nearly half their hours are non-billable. That could indicate internal projects, poor time tracking habits, or work on non-contracted services.
EVALUATE
TOPN(15,
SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"BillableHrs", SUM('BI_Autotask_Time_Entries'[Billable Hours]),
"NonBillableHrs", SUM('BI_Autotask_Time_Entries'[Non billable Hours]),
"TicketCount", DISTINCTCOUNT('BI_Autotask_Time_Entries'[ticket_id])
),
[TotalHours], DESC
)
ORDER BY [TotalHours] DESCSegmented view of each engineer's hours showing the billable/non-billable split at a glance
The portfolio-wide billable rate sits at 75.6%. Three engineers fall below 65%, which pulls the overall number down. If those three engineers moved from 55% to 70% billable, the total billable hours would increase by roughly 1,200 hours, worth significant revenue at typical MSP hourly rates.
EVALUATE
ROW(
"TotalCIs", COUNTROWS('BI_Autotask_Configuration_Items'),
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"TotalTimeEntries", COUNTROWS('BI_Autotask_Time_Entries'),
"TotalHoursWorked", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"TotalBillableHrs", SUM('BI_Autotask_Time_Entries'[Billable Hours]),
"TotalNonBillableHrs", SUM('BI_Autotask_Time_Entries'[Non billable Hours])
)Comparing the three largest CI categories by count, time share, and implied effort per unit
| CI Type | CI Count | % of Total CIs | Volume | Typical Effort |
|---|---|---|---|---|
| Workstation | 6,933 | 50.3% | High Volume | Low per unit |
| Other/Network | 2,734 | 19.9% | Medium Volume | Medium per unit |
| Server | 1,461 | 10.6% | Medium Volume | High per unit |
Workstations generate the most tickets by sheer volume, but individual workstation issues tend to be quick fixes: password resets, software installs, peripheral problems. The time per workstation is low.
Servers tell a different story. With only 1,461 units (10.6% of the CMDB), they tend to consume disproportionate engineer time per unit. Server issues are more complex: patching, performance tuning, backup failures, and security incidents often involve senior engineers working longer sessions.
Network devices (2,734 items including switches, firewalls, and access points individually, plus the "Other" catch-all) sit in the middle. They generate fewer tickets than workstations but the issues tend to affect multiple users, which increases urgency and time spent.
Engineers C, I, and G account for 2,273 non-billable hours combined, nearly half their total output. This pattern suggests either internal project work that should be tracked separately, or a time-entry discipline issue. Either way, it represents recoverable revenue if addressed.
Engineer D handles 2,613 tickets in 2,050 hours (0.78 hrs/ticket), while Engineer C handles just 99 tickets in 2,060 hours (20.8 hrs/ticket). This difference is too large to be explained by ticket complexity alone. It likely reflects different roles: Engineer C may handle projects or escalations, while Engineer D handles high-volume service desk work. Tagging time entries by work type would clarify the picture.
The industry average for MSP billable utilization typically ranges from 60% to 70%. At 75.6%, this team is performing well overall. The opportunity is not to push the average higher across the board, but to bring the bottom performers closer to the team median.
1. Audit non-billable hours for Engineers C, G, and I. Review their time entries for the past 90 days. If the non-billable time is legitimate internal work (documentation, training, tooling), create separate project codes so it does not inflate the service delivery non-billable figure. If it is billable work logged incorrectly, fix the categorization.
2. Introduce per-CI-type time tracking. Tag time entries with the CI type they relate to. This lets you calculate actual hours per workstation vs hours per server, which in turn feeds pricing models and staffing decisions. Without this tag, you are estimating. With it, you have data.
3. Investigate the long-tail CI types. Azure AVD (22), Dockingstations (20), Monitors (15), and Conference setups (14) are low count but may generate tickets that are hard to categorize. Make sure these CIs are properly linked to tickets so time tracking is accurate.
4. Set billable rate targets per engineer role. Service desk engineers should target 80%+ billable. Project engineers or escalation specialists might reasonably sit at 60-65%. The key is having a target per role, not a blanket number that penalizes engineers doing necessary non-billable work.
5. Review Domain Registration as a CI type. With 954 items, domain registrations make up 6.9% of the CMDB. These rarely generate support tickets. Consider whether they belong in the same CI tracking as hardware assets, or whether a separate register would reduce noise in your asset reports.
A configuration item is any tracked asset in your Autotask CMDB (Configuration Management Database). This includes workstations, servers, firewalls, switches, mobile devices, printers, and other hardware or logical items. Each CI is linked to a company and can be associated with tickets and time entries.
Time entries in Autotask are logged against tickets. Tickets can be associated with one or more CIs. Proxuma Power BI joins these tables so you can see which CI types generate the most engineer time. If a ticket is not linked to a CI, those hours will not appear in per-device breakdowns.
Billable hours are time entries marked as billable in Autotask, meaning they can be invoiced to the client. Non-billable hours include internal work, training, meetings, and any time logged against non-contracted work. The classification comes directly from the Autotask time entry settings.
The average is calculated across all 13,769 CIs, many of which never generate a ticket. Domain registrations (954 items), monitors, and docking stations rarely need engineer time. When you filter to only CIs with at least one associated ticket, the average per device goes up significantly. This report shows the portfolio-wide average as a baseline.
Yes. The DAX queries in this report run against the full dataset. To filter by client, add a FILTER clause on the company name column. To filter by date range, add a filter on the time entry date. Proxuma Power BI supports both through the AI interface, so you can ask a follow-up question like "show me hours per device for Client A in Q1 2026."
Yes. Connect Proxuma Power BI to your Autotask account, 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.
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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