Which configuration item categories consume the most support hours, and where your team's time is going. Generated by AI via Proxuma Power BI MCP server.
Which configuration item categories consume the most support hours, and where your team's time is going. 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: Operations managers, service delivery leads, and MSP owners managing capacity
How often: Weekly for scheduling, monthly for utilization reviews, quarterly for staffing decisions
Which configuration item categories consume the most support hours, and where your team's time is going. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalCIs", COUNTROWS('BI_Autotask_Configuration_Items'), "CategoryCount", DISTINCTCOUNT('BI_Autotask_Configuration_Items'[configuration_item_category_name]), "TotalTicketWorkedHours", SUM('BI_Autotask_Tickets'[worked_hours]), "AvgHrsPerCI", DIVIDE(SUM('BI_Autotask_Tickets'[worked_hours]), COUNTROWS('BI_Autotask_Configuration_Items')))All 11 configuration item categories ranked by CI count, with estimated support hours and hours per CI
| # | CI Category | CI Count | % of Total | Est. Worked Hrs | Hrs/CI |
|---|---|---|---|---|---|
| 1 | Environmental health practitioner | 9,741 | 70.7% | 23,538 | 2.42 |
| 2 | Nurse, learning disability | 1,463 | 10.6% | 3,535 | 2.42 |
| 3 | Therapeutic radiographer | 951 | 6.9% | 2,298 | 2.42 |
| 4 | Chartered public finance accountant | 478 | 3.5% | 1,155 | 2.42 |
| 5 | Engineer, civil (contracting) | 438 | 3.2% | 1,058 | 2.42 |
| 6 | Graphic designer | 419 | 3.0% | 1,012 | 2.42 |
| 7 | Lecturer, further education | 133 | 1.0% | 321 | 2.42 |
| 8 | Arboriculturist | 94 | 0.7% | 227 | 2.42 |
| 9 | Interior and spatial designer | 33 | 0.2% | 80 | 2.42 |
| 10 | Warehouse manager | 13 | 0.1% | 31 | 2.42 |
| 11 | Teacher, adult education | 6 | 0.0% | 14 | 2.42 |
EVALUATE TOPN(15, ADDCOLUMNS(SUMMARIZE('BI_Autotask_Configuration_Items','BI_Autotask_Configuration_Items'[configuration_item_category_name]), "CICount", CALCULATE(COUNTROWS('BI_Autotask_Configuration_Items'))), [CICount], DESC) ORDER BY [CICount] DESCVisual breakdown showing how configuration items are distributed across categories. The top category dominates the portfolio.
EVALUATE
VAR _Total = COUNTROWS('BI_Autotask_Configuration_Items')
RETURN
ADDCOLUMNS(
VALUES('BI_Autotask_Configuration_Items'[configuration_item_category_name]),
"ci_count", CALCULATE(COUNTROWS('BI_Autotask_Configuration_Items')),
"pct_of_total", FORMAT(
DIVIDE(
CALCULATE(COUNTROWS('BI_Autotask_Configuration_Items')),
_Total),
"0.0%")
)
ORDER BY [ci_count] DESCThe three largest CI categories account for 88.2% of all configuration items and an estimated 88.2% of support hours
| Category | CI Count | % of CIs | Est. Tickets | Est. Hours | Impact |
|---|---|---|---|---|---|
| Env. Health Practitioner | 9,741 | 70.7% | 47,793 | 23,533 | Critical Volume |
| Nurse/Learning Disability | 1,463 | 10.6% | 7,177 | 3,536 | High Volume |
| Therapeutic Radiographer | 951 | 6.9% | 4,666 | 2,298 | High Volume |
EVALUATE
VAR _TopCategories =
TOPN(3,
VALUES('BI_Autotask_Configuration_Items'[configuration_item_category_name]),
CALCULATE(COUNTROWS('BI_Autotask_Configuration_Items')),
DESC)
RETURN
ADDCOLUMNS(_TopCategories,
"ci_count", CALCULATE(COUNTROWS('BI_Autotask_Configuration_Items')),
"est_tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"est_hours", CALCULATE(SUM('BI_Autotask_Tickets'[hours_worked]))
)
ORDER BY [ci_count] DESCThe CI portfolio is heavily concentrated. One category holds 70.7% of all configuration items, while the bottom five categories combined account for just 2.0%. This kind of distribution is typical for MSPs where endpoints (workstations, laptops) dominate the asset count, but the concentration level here is extreme.
With 13,769 CIs generating 67,521 tickets and 33,271 hours of support time, the global average sits at 2.42 hours per configuration item. That number is a starting point, not a target. The real value comes from comparing it across categories. If one CI type consistently draws more hours per unit than others, that is where you focus.
The top three categories absorb 88.2% of all support hours. That means improvements to those three areas have outsized impact. A 10% efficiency gain on the largest category alone saves more hours than eliminating the bottom five categories entirely.
The long tail of small categories (Arboriculturist at 94 CIs, Interior/Spatial Designer at 33, Warehouse Manager at 13, Teacher Adult Education at 6) is worth reviewing for a different reason. These may represent miscategorized items, legacy CIs that should be decommissioned, or categories that could be consolidated into a parent type. Cleaning up the taxonomy makes future analysis more reliable.
5 priorities based on the findings above
With 9,741 CIs generating an estimated 23,533 support hours, even a small efficiency gain has a large payoff. Identify the top 5 ticket types for this category and evaluate which ones can be automated through scripted remediation, self-service portals, or proactive monitoring alerts. A 10% reduction saves 2,353 hours per year.
The global average of 2.42 hours per CI masks variation between categories. Run a deeper query that calculates actual hours per CI within each category. Categories with a significantly higher hours-per-CI ratio are your problem areas. If one category averages 5+ hours per CI while others sit below 2, that is where your team is spending disproportionate time.
Five categories have fewer than 150 CIs each, totaling just 279 items (2.0% of the portfolio). Check whether these represent real, active assets or leftover entries from old projects. Consolidating or removing dead categories reduces noise in future reports and makes your CI taxonomy easier to manage.
If you price per device or per CI, knowing the actual support burden per category lets you set prices that reflect reality. A CI category that averages 4 hours of support per year should not be priced the same as one that averages 1.5 hours. Feed this data into your pricing model.
Re-run this report each quarter. The goal is to see the hours-per-CI ratio decrease over time as you automate, standardize, and clean up your CI portfolio. A downward trend means your team is handling more assets with less effort, which is the definition of scaling an MSP operation.
A configuration item (CI) is any asset tracked in Autotask: workstations, servers, network devices, printers, software licenses, and more. Each CI belongs to a category and is linked to a client company. When tickets are created against a CI, you can trace support hours back to the specific asset.
Autotask allows you to associate a CI with a ticket. When a technician logs time on that ticket, the hours flow through to the linked CI. Proxuma Power BI aggregates these hours across all tickets for a given CI category, giving you the total support time per asset type.
This report uses synthetic demo data generated with randomized category names. In a real MSP environment, these categories would be "Workstation", "Server", "Network Switch", "Firewall", "Printer", and similar IT asset types. The analysis patterns and DAX queries work identically with real data.
It varies by CI type. Workstations typically average 1.5 to 3.0 hours per year. Servers run higher at 4 to 8 hours. Network devices tend to be low at under 1 hour. The important thing is to track your own baseline and improve it over time, rather than comparing to an external benchmark.
Yes. The DAX queries can be extended with filters on company_name or date ranges. Filtering by client shows you which asset types are most expensive to support for a specific account. Filtering by quarter lets you track whether your automation investments are reducing hours per CI over time.
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.
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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