“Estimated vs Actual Hours”
Autotask PSA Datto RMM Datto Backup Microsoft 365 SmileBack HubSpot IT Glue All reports
AI-GENERATED REPORT
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How accurate are ticket time estimates? Where do estimated hours differ most from actual worked hours by queue?

Estimated vs Actual Hours

A data-driven analysis of estimated vs actual hours from your Power BI environment, with breakdowns and actionable findings.

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
Ready in < 15 min
Want to run this report against your own Autotask PSA data?
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Estimated vs Actual Hours

This report analyzes estimated vs actual hours using data from Autotask PSA.

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

Time saved
Calculating utilization from time entries and ticket data manually is tedious. This report does it automatically.
Capacity insight
See who is overloaded, who has bandwidth, and where bottlenecks form.
Staffing data
Evidence-based decisions about hiring, scheduling, and workload distribution.
Report categoryResource & Capacity
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
AudienceOperations managers, service delivery leads
Where to find this in Proxuma
Power BI › Resources › Estimated vs Actual Hours
What you can measure in this report
Summary Metrics
Hours by Resource
Hours by Company
Billable vs Non-Billable
Monthly Hours Trend
Analysis
Recommended Actions
Frequently Asked Questions
TOTAL HOURS
AI-Generated Power BI Report
Estimated vs Actual Hours

A data-driven analysis of estimated vs actual hours from your Power BI environment, with breakdowns and actionable findings.

Demo Report: This report uses synthetic data to demonstrate AI-generated insights from Proxuma Power BI. The structure, DAX queries, and analysis reflect real MSP data patterns.
1.0 Summary Metrics
TOTAL HOURS
25,868
15 resources logged
View DAX Query — Summary query 
-- Combined summary metrics from Power BI dataset
What are these DAX queries? DAX (Data Analysis Expressions) is the formula language Power BI uses to query data. Each collapsible section below shows the exact query the AI wrote and ran. You can copy any query and run it in Power BI Desktop against your own dataset.
1.0 Hours by Resource

Hours logged per resource from the demo dataset

Brandon Lynn
1,343
Brandon Bishop
1,361
Daniel Daniels
1,418
Gregory Horn
1,504
Elizabeth Ortega
1,433
Jennifer King
1,584
Jeremy White
1,492
Dr. Amber Ayala DVM
2,399
Kevin Allen
2,060
James Li
2,135
ResourceHours
Brandon Lynn1,343.7
Brandon Bishop1,361.5
Daniel Daniels1,418.4
Gregory Horn1,504.5
Elizabeth Ortega1,433.4
Jennifer King1,584.5
Jeremy White1,492.5
Dr. Amber Ayala DVM2,399.8
Kevin Allen2,060.1
James Li2,136.0
Maxwell Reed2,050.3
Chelsea Thomas1,779.6
David Hunt1,862.2
Andrew Roberts1,887.7
Jerry Mcfarland1,554.0
View DAX Query — Hours by Resource query 
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked])), [Hours], DESC)
2.0 Hours by Company

Total hours logged per company

Richards, Bell and Christ
782
Wu-Jackson
962
Price-Gomez
864
Martin Group
2,217
Thompson, Contreras and R
1,006
Doyle-Contreras
961
Clements, Pham and Garcia
866
None
7,264
Lewis LLC
2,801
Little Group
3,791
CompanyHours
Richards, Bell and Christensen782.4
Wu-Jackson962.0
Price-Gomez864.9
Martin Group2,217.0
Thompson, Contreras and Rios1,006.1
Doyle-Contreras961.9
Clements, Pham and Garcia866.3
-7,264.2
Lewis LLC2,801.1
Little Group3,791.4
Craig-Huynh4,370.4
Rivers, Rogers and Mitchell1,661.8
Burke, Armstrong and Morgan1,312.3
Wall PLC1,696.9
Ramos Group1,170.6
View DAX Query — Hours by Company query 
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Companies'[company_name], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked])), [Hours], DESC)
3.0 Billable vs Non-Billable

Split between billable and non-billable hours

75.6%
Billable (38,363h)
24.4%
Non-Billable (12,387h)
Non-BillableHours
-38,363.8
True12,387.8
View DAX Query — Billable vs Non-Billable query 
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[is_non_billable], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked]))
4.0 Monthly Hours Trend

Monthly hours trend over the observed period

4,1923,6493,1062,5632,021 2,5344,0032,115 202502202504202506202508202510202512202601
MonthHours
2025022,534.3
2025033,330.5
2025043,588.0
2025053,314.9
2025063,198.0
2025073,536.6
2025082,686.4
2025093,864.6
2025104,003.3
2025113,314.2
2025123,247.4
2026012,115.7
View DAX Query — Monthly Hours Trend query 
EVALUATE TOPN(12, SUMMARIZECOLUMNS('BI_Common_Dim_Date'[year_month], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked])), 'BI_Common_Dim_Date'[year_month], DESC)
6.0 Analysis

What the data is telling us

The team logged 25,868 hours across 15 resources, averaging 1,724 hours per person. Look for outliers on both ends: engineers logging significantly more may be overloaded, while those with low hours may have logging compliance issues.

7.0 Recommended Actions
✓

1. Schedule Recurring Review

Set up a weekly or monthly review of estimated vs actual hours metrics. Trends matter more than snapshots. Use the DAX queries in this report as your starting point.

✓

2. Connect Your Own Data

This report uses demo data. Connect Proxuma Power BI to your own Autotask PSA to generate this analysis from your real numbers.

8.0 Frequently Asked Questions
What data sources does the Estimated vs Actual Hours report use?

This report pulls data from PSA through the Proxuma Power BI integration, using DAX queries against the live data model.

How often is this data refreshed?

The underlying Power BI dataset refreshes daily. Reports can be regenerated at any time for the latest figures.

Can I customize this estimated vs actual hours report?

Yes. Proxuma reports are fully customizable. You can modify the DAX queries, add new sections, or adjust the analysis to match your specific MSP needs.

Proxuma Power BI · AI-Generated Report · proxuma.io/powerbi Generated in < 15 minutes via MCP · March 2026
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