Where your configuration items live, which categories dominate, and where classification gaps may be hiding. Generated by AI via Proxuma Power BI MCP server.
Where your configuration items live, which categories dominate, and where classification gaps may be hiding. 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
Where your configuration items live, which categories dominate, and where classification gaps may be hiding. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("Total CIs", COUNTROWS('BI_Autotask_Configuration_Items'), "Active CIs", COUNTROWS(FILTER('BI_Autotask_Configuration_Items', 'BI_Autotask_Configuration_Items'[status] = "Active")), "Distinct Categories", DISTINCTCOUNT('BI_Autotask_Configuration_Items'[configuration_item_category_name]), "Distinct Companies", DISTINCTCOUNT('BI_Autotask_Configuration_Items'[company_id]))All 11 categories ranked by number of configuration items, with percentage share of total
EVALUATE ADDCOLUMNS(GROUPBY('BI_Autotask_Configuration_Items', 'BI_Autotask_Configuration_Items'[configuration_item_category_name], "CI_Count", COUNTX(CURRENTGROUP(), 'BI_Autotask_Configuration_Items'[configuration_item_id])), "Companies", CALCULATE(DISTINCTCOUNT('BI_Autotask_Configuration_Items'[company_id]))) ORDER BY [CI_Count] DESCFull breakdown per category with item count, percentage share, and relative size classification
| # | Category | CIs | % Share | Classification | Cumulative % |
|---|---|---|---|---|---|
| 1 | Category A | 9,741 | 70.7% | Dominant | 70.7% |
| 2 | Category B | 1,463 | 10.6% | Major | 81.4% |
| 3 | Category C | 951 | 6.9% | Major | 88.3% |
| 4 | Category D | 478 | 3.5% | Medium | 91.7% |
| 5 | Category E | 438 | 3.2% | Medium | 94.9% |
| 6 | Category F | 419 | 3.0% | Medium | 97.9% |
| 7 | Category G | 133 | 1.0% | Small | 98.9% |
| 8 | Category H | 94 | 0.7% | Small | 99.6% |
| 9 | Category I | 33 | 0.2% | Micro | 99.8% |
| 10 | Category J | 13 | 0.1% | Micro | 99.9% |
| 11 | Category K | 6 | 0.04% | Micro | 100% |
EVALUATE
VAR _CatSummary =
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Configuration_Items,
BI_Autotask_Configuration_Items[ci_category_name]
),
"CICount", COUNTROWS(BI_Autotask_Configuration_Items),
"PctShare", DIVIDE(
COUNTROWS(BI_Autotask_Configuration_Items),
CALCULATE(
COUNTROWS(BI_Autotask_Configuration_Items),
ALL(BI_Autotask_Configuration_Items[ci_category_name])))
)
RETURN
_CatSummary
ORDER BY [CICount] DESCHow many of the 205 companies have CIs in each category. Low company counts in a category may indicate limited adoption or niche usage.
| Category | Companies | Avg CIs per Company | Company Coverage | Adoption |
|---|---|---|---|---|
| Category A | 198 | 49.2 | 96.6% | Universal |
| Category B | 152 | 9.6 | 74.1% | Broad |
| Category C | 134 | 7.1 | 65.4% | Broad |
| Category D | 89 | 5.4 | 43.4% | Moderate |
| Category E | 76 | 5.8 | 37.1% | Moderate |
| Category F | 71 | 5.9 | 34.6% | Moderate |
| Category G | 28 | 4.8 | 13.7% | Limited |
| Category H | 22 | 4.3 | 10.7% | Limited |
| Category I | 11 | 3.0 | 5.4% | Niche |
| Category J | 7 | 1.9 | 3.4% | Niche |
| Category K | 4 | 1.5 | 2.0% | Niche |
EVALUATE
ADDCOLUMNS(
SUMMARIZE(
BI_Autotask_Configuration_Items,
BI_Autotask_Configuration_Items[ci_category_name]
),
"CompanyCount", DISTINCTCOUNT(
BI_Autotask_Configuration_Items[company_id]),
"AvgCIsPerCompany", DIVIDE(
COUNTROWS(BI_Autotask_Configuration_Items),
DISTINCTCOUNT(BI_Autotask_Configuration_Items[company_id])),
"CompanyCoverage", DIVIDE(
DISTINCTCOUNT(BI_Autotask_Configuration_Items[company_id]),
CALCULATE(
DISTINCTCOUNT(BI_Autotask_Configuration_Items[company_id]),
ALL(BI_Autotask_Configuration_Items[ci_category_name])))
)
ORDER BY [CompanyCount] DESCThe single most important number in this report is 70.7%. That is the share of all configuration items sitting in Category A. Nearly 10,000 of 13,769 CIs are in one bucket. When one category contains more than two-thirds of your entire CMDB, the classification system is either too broad, the default category is absorbing items that should be split out, or the onboarding process is not enforcing granular categorization.
Category A is also nearly universal: 198 out of 205 companies have CIs there, with an average of 49 items per company. That high average suggests this is the catch-all. Compare it to Category B, where 152 companies average only 9.6 items each. Category B appears to be used deliberately, while Category A may be where items land when nobody specifies otherwise.
The long tail is worth examining. Categories G through K together account for just 279 items (2.0% of total) across a small number of companies. Categories J and K hold 13 and 6 items respectively, used by fewer than 10 companies each. These micro-categories may serve a real purpose, or they may be duplicates of larger categories that were created once and never cleaned up.
On the positive side, the top three categories cover 88.3% of all CIs, and the top six cover 97.9%. This means the core classification structure works for the majority of items. The question is whether the remaining items are properly placed or simply never reviewed.
The company coverage numbers also reveal a gap. Categories D, E, and F are each used by fewer than half of all companies. If these categories represent asset types that every MSP client should have (servers, network equipment, backup devices), the 55-65% of companies missing from those categories may have untracked assets.
5 priorities based on the findings above
With 9,741 items (70.7% of total), Category A is almost certainly absorbing CIs that belong elsewhere. Pull a random sample of 50 items from Category A and check whether they belong in Category B, C, or D instead. If more than 20% are misclassified, you have a systemic onboarding problem. Fix the default category assignment in Autotask so new CIs require explicit categorization.
Categories I, J, and K hold a combined 52 items across 22 company assignments. Check whether these categories overlap with larger ones. If Category J with 13 items is a subset of Category B with 1,463, merge them. Fewer categories with clear definitions beats many categories that nobody uses consistently.
Between 55% and 65% of companies have no CIs in these categories. If these categories represent standard asset types, the missing companies may have untracked infrastructure. Cross-reference the company list against Categories D-F and check whether those clients genuinely have no assets in those types, or whether the assets exist but were never entered.
The concentration in Category A often comes from bulk imports during client onboarding. When a technician imports 50 CIs from a network scan, they land in the default category unless someone manually re-categorizes each one. Build an onboarding checklist that requires category assignment before a new client's CIs are marked as verified.
Run this report quarterly and track whether the Category A share is decreasing. A healthy CMDB should see gradual re-distribution as items are properly classified. Set a target: reduce Category A from 70.7% to under 60% within two quarters by reclassifying the top 1,000 items that were flagged as miscategorized.
Configuration items are pulled from Autotask PSA through the Proxuma Power BI connector. The BI_Autotask_Configuration_Items table contains every CI record with its category, company assignment, and status. The AI runs DAX queries to count, group, and rank items by category.
A dominant category usually means it is the default assignment in Autotask. When CIs are imported through network scans or bulk entry, they land in the default category unless someone manually reclassifies them. Over time, this creates a catch-all bucket. The fix is to review your Autotask CI import settings and require explicit category selection.
It depends on whether those categories serve a distinct purpose. If Category K with 6 items tracks a specific asset type that no other category covers, keep it. If it duplicates or overlaps with a larger category, merge them. The goal is fewer, well-defined categories that your team actually uses consistently.
Many compliance frameworks (SOC 2, ISO 27001, CIS Controls) require accurate asset inventories categorized by type. A CMDB where 70% of items sit in one generic category is hard to defend in an audit. Clean categorization makes compliance reporting faster and more credible.
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.
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