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Which project phases are significantly over budget?

8 Phases Are Over 100% Above Budget: Phase Budget Tracking

Comparing estimated vs actual hours across all project phases to identify budget overruns.

Built from: Autotask PSA

How this report was made

Autotask PSA

Multiple data sources combined

Proxuma Power BI

Pre-built MSP semantic model, 50+ measures

AI via MCP

Claude or ChatGPT writes DAX queries, executes them, formats output

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8 Phases Are Over 100% Above Budget: Phase Budget Tracking

Comparing estimated vs actual hours across all project phases to identify budget overruns.

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: Project managers, operations leads, and MSP owners tracking delivery

How often: Weekly for status updates, milestone dates for escalation, monthly for portfolio review

Time saved

Assembling project status from multiple tools and conversations takes hours. This report pulls it together.

Delivery visibility

Milestone progress, budget variance, and timeline risks across all active projects.

Client communication

Project status data formatted for client-facing updates and steering meetings.

Report categoryProject Management

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

AudienceProject managers, operations leads

Where to find this in Proxuma

Power BI › Projects › 8 Phases Are Over 100% Above Budget: ...

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What you can measure in this report

Summary Metrics

Phases With the Worst Budget Overruns

Phases Coming In Under Budget

Budget Variance Distribution

Key Findings

Recommendations

Frequently Asked Questions

PHASES ANALYZED

OVER BUDGET >100%

ON TRACK (±10%)

BIGGEST OVERRUN

AI-Generated Power BI Report

8 Phases Are Over 100% Above Budget:
Phase Budget Tracking

Comparing estimated vs actual hours across all project phases to identify budget overruns.

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

PHASES ANALYZED

Across all active projects

OVER BUDGET >100%

16% of all phases

ON TRACK (±10%)

30% of all phases

BIGGEST OVERRUN

+6,820%

Phase 9780: 2.5h est, 173.0h actual

View DAX Query: Phase budget data with estimated vs actual minutes

EVALUATE
TOPN(50,
    ADDCOLUMNS(
        VALUES('BI_Autotask_Phases'[phase_id]),
        "proxuma_phase_id", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_phase_id])),
        "start_date", CALCULATE(MAX('BI_Autotask_Phases'[start_date])),
        "end_date", CALCULATE(MAX('BI_Autotask_Phases'[end_date])),
        "est_minutes", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_estimated_in_minutes])),
        "act_minutes", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_actual_in_minutes]))
    ),
    [act_minutes], DESC
)

2.0 Phases With the Worst Budget Overruns

All phases where actual hours exceed the estimate by more than 100%. Sorted by variance percentage, highest first.

Metric	Value
Projects	279
Est Hours	10,066
Worked Hours	10,216
Variance	-150 hours

Reading this table: Phase 9780 was estimated at 2.5 hours. The team logged 173 hours against it. That is a 6,820% overrun. Three of the eight worst offenders had original estimates under 3 hours, which suggests either incomplete scoping or phases used as catch-all time entries.

View DAX Query: Over-budget phases ranked by variance

EVALUATE ROW("TotalProjects", COUNTROWS('BI_Autotask_Projects'), "TotalEstHours", SUM('BI_Autotask_Projects'[estimated_hours]), "TotalWorkedHours", SUM('BI_Autotask_Projects'[worked_hours]), "TotalVariance", SUM('BI_Autotask_Projects'[variance_hours]))

3.0 Phases Coming In Under Budget

Phases where the team used fewer hours than estimated. Sorted by the largest savings first.

Phase ID	Estimated (h)	Actual (h)	Variance	Status
52654	536.3	238.0	-55.6%	Under budget
35475	1,586.7	1,205.0	-24.1%	Under budget
85073	338.8	269.0	-20.6%	Under budget
71189	628.0	608.0	-3.2%	On track
32084	206.9	208.0	+0.5%	On track

Note: Phase 52654 came in 55.6% under budget (298.3 hours saved). Phase 35475 saved 381.7 hours against a 1,586.7-hour estimate. These phases either benefited from strong scoping or were descoped during delivery. Worth reviewing to separate genuine efficiency from scope reduction.

View DAX Query: Under-budget phases

EVALUATE
TOPN(50,
    ADDCOLUMNS(
        VALUES('BI_Autotask_Phases'[phase_id]),
        "proxuma_phase_id", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_phase_id])),
        "start_date", CALCULATE(MAX('BI_Autotask_Phases'[start_date])),
        "end_date", CALCULATE(MAX('BI_Autotask_Phases'[end_date])),
        "est_minutes", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_estimated_in_minutes])),
        "act_minutes", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_actual_in_minutes]))
    ),
    [act_minutes], DESC
)

4.0 Budget Variance Distribution

How all 50 phases break down by budget performance category

All Phases

Under budget (20) On track ±10% (15) Over 10-100% (7) Over >100% (8)

On track or
under budget

Severely over
budget (>100%)

Moderately over
budget (10-100%)

View DAX Query: Budget variance distribution

EVALUATE
TOPN(50,
    ADDCOLUMNS(
        VALUES('BI_Autotask_Phases'[phase_id]),
        "proxuma_phase_id", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_phase_id])),
        "start_date", CALCULATE(MAX('BI_Autotask_Phases'[start_date])),
        "end_date", CALCULATE(MAX('BI_Autotask_Phases'[end_date])),
        "est_minutes", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_estimated_in_minutes])),
        "act_minutes", CALCULATE(MAX('BI_Autotask_Phases'[proxuma_actual_in_minutes]))
    ),
    [act_minutes], DESC
)

5.0 Key Findings

Low estimates create the biggest blowouts

The three phases with the highest percentage overruns (9780, 7979, 82395) all had original estimates under 3 hours. When a phase is budgeted at 1-2.5 hours, any real work immediately produces a 1,000%+ variance. These are likely phases that were created as placeholders or scoped without real task breakdowns.

Large phases are overrunning too, just less visibly

Phase 9727 (50.6h estimate, 527.0h actual) and Phase 41160 (484.9h estimate, 729.0h actual) represent the most expensive overruns in absolute hours. Combined, they consumed 720.5 more hours than planned. The percentage looks lower at 50-942%, but the dollar impact is far larger than the smaller phases.

70% of phases are within acceptable range

35 out of 50 phases are either under budget or within 10% of the estimate. The overrun problem is concentrated in a relatively small group. Fixing the estimation process for those 15 problematic phases would bring the portfolio into much better shape without changing how the other 35 are managed.

6.0 Recommendations

1. Set a minimum estimate threshold. Any phase with an estimate under 4 hours should be flagged during project setup. If a phase genuinely needs only 1-2 hours, it probably belongs as a task inside a larger phase, not a standalone phase with its own budget line.

2. Add a budget alert at 75% consumption. Phases 9727 and 41160 burned through hundreds of extra hours before anyone noticed. An automated alert when a phase hits 75% of its estimated hours gives project leads time to reassess scope or adjust the estimate before the overrun becomes permanent.

3. Review the 8 severely over-budget phases individually. Each of the 8 phases above 100% variance needs a brief root cause review. The answer will fall into one of three categories: bad initial estimate, scope change that was never re-estimated, or time logged to the wrong phase. Each requires a different fix.

4. Track under-budget phases for estimation accuracy. Phase 52654 came in at 55.6% under budget (298 hours unspent). That is either a sign of great efficiency or a sign the estimate was inflated. If estimates are consistently high, they distort capacity planning and make the portfolio look healthier than it is.

5. Run this report monthly. A single snapshot shows the current state. Running this every month turns it into a trend line: are overruns growing or shrinking? Are the same projects showing up repeatedly? Monthly comparison is what separates a data point from an actual management tool.

7.0 Frequently Asked Questions

Where does the estimated vs actual hours data come from?

Autotask PSA stores both the estimated and actual minutes per project phase. Proxuma Power BI pulls these fields through the Autotask connector. The AI divides minutes by 60 to display hours and calculates the variance percentage as (actual - estimated) / estimated x 100.

Why do some phases show thousands of percent variance?

Percentage variance amplifies when the original estimate is very small. A phase estimated at 2.5 hours that consumes 173 hours produces a 6,820% overrun. In absolute terms, 170.5 extra hours is serious but manageable. The percentage is eye-catching, but the dollar impact depends on the actual hours, not the percentage.

Does this include completed projects or only active ones?

This report queries the top 50 phases by actual minutes logged, regardless of project status. That means it includes active, completed, and on-hold phases. To filter by status, add a project status filter to the DAX query.

Can I drill into a specific project to see all its phases?

Yes. Add a filter on the project ID in the DAX query to isolate a single project. Proxuma Power BI can also generate a per-project report that breaks down every phase with its budget status, timeline, and resource allocation.

Can I run this report against my own Autotask data?

Yes. Connect Proxuma Power BI to your Autotask PSA 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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