Which project tasks are behind schedule, which projects carry the most overdue work, and where actual hours already exceed estimates. Generated by AI via Proxuma Power BI MCP server.
Which project tasks are behind schedule, which projects carry the most overdue work, and where actual hours already exceed estimates. 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: Service desk managers, dispatch leads, and operations teams
How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning
Which project tasks are behind schedule, which projects carry the most overdue work, and where actual hours already exceed estimates. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
VAR today = TODAY()
RETURN ROW(
"TotalTasks", COUNTROWS('BI_Autotask_Tasks'),
"ActiveNotStarted", CALCULATE(COUNTROWS('BI_Autotask_Tasks'), 'BI_Autotask_Tasks'[status_name] IN {"New","Planned","Waiting Customer","Waiting for third party"}),
"InProgress", CALCULATE(COUNTROWS('BI_Autotask_Tasks'), 'BI_Autotask_Tasks'[status_name] = "In progress"),
"StartingThisWeek", CALCULATE(COUNTROWS('BI_Autotask_Tasks'), 'BI_Autotask_Tasks'[status_name] <> "Complete", 'BI_Autotask_Tasks'[start_date] >= today, 'BI_Autotask_Tasks'[start_date] <= today + 7),
"StartingNext30Days", CALCULATE(COUNTROWS('BI_Autotask_Tasks'), 'BI_Autotask_Tasks'[status_name] <> "Complete", 'BI_Autotask_Tasks'[start_date] >= today, 'BI_Autotask_Tasks'[start_date] <= today + 30),
"OverdueOpen", CALCULATE(COUNTROWS('BI_Autotask_Tasks'), 'BI_Autotask_Tasks'[status_name] <> "Complete", 'BI_Autotask_Tasks'[end_date] < today),
"DueThisWeekOpen", CALCULATE(COUNTROWS('BI_Autotask_Tasks'), 'BI_Autotask_Tasks'[status_name] <> "Complete", 'BI_Autotask_Tasks'[end_date] >= today, 'BI_Autotask_Tasks'[end_date] <= today + 7)
)| # | Project | Client | Overdue Tasks | Worked Hours Logged |
|---|---|---|---|---|
| 1 | Project Happy | Mitchell-Everett | 132 | — |
| 2 | Project Bag | Mitchell-Everett | 51 | — |
| 3 | Project Anyone | Ramos Group | 42 | 181.4h |
| 4 | Project Set | Smith and Sons | 40 | 101.5h |
| 5 | Project Him | Doyle-Contreras | 11 | 41.3h |
| 6 | Project Professional | Gonzales and Sons | 11 | — |
| 7 | Project Feel | Wu-Jackson | 11 | 266.8h |
| 8 | Project Allow | Mitchell-Everett | 11 | — |
| 9 | Project Professional | Keith, Keller and Adams | 10 | — |
| 10 | Project Next | Little Group | 7 | 40.0h |
| 11 | Project Paper | Green PLC | 4 | 22.8h |
| 12 | Project Mr | Rivers, Rogers and Mitchell | 4 | 208.1h |
| 13 | Project Husband | Martin Group | 4 | 11.8h |
| 14 | Project How | Lee-Dalton | 4 | 1.5h |
| 15 | Project Owner | Clements, Pham and Garcia | 4 | 27.0h |
The gap between top and bottom performers requires attention.
EVALUATE
TOPN(15,
ADDCOLUMNS(
SUMMARIZE(FILTER('BI_Autotask_Tasks','BI_Autotask_Tasks'[status_name] <> "Complete" && 'BI_Autotask_Tasks'[end_date] < TODAY()), 'BI_Autotask_Tasks'[project_name],'BI_Autotask_Tasks'[company_name]),
"OverdueTasks", CALCULATE(COUNTROWS('BI_Autotask_Tasks'),'BI_Autotask_Tasks'[status_name] <> "Complete", 'BI_Autotask_Tasks'[end_date] < TODAY()),
"WorkedHours", CALCULATE(SUM('BI_Autotask_Tasks'[worked_hours]),'BI_Autotask_Tasks'[status_name] <> "Complete", 'BI_Autotask_Tasks'[end_date] < TODAY())
),
[OverdueTasks], DESC
)Improvement from 81.8% to 87.4% over three quarters.
EVALUATE
SUMMARIZECOLUMNS(
BI_Autotask_Tickets[created_month],
"NewTickets", COUNTROWS(BI_Autotask_Tickets),
"Resolved", CALCULATE(COUNTROWS(BI_Autotask_Tickets), BI_Autotask_Tickets[status] = "Complete"),
"SLARate", [AT - SLA Met Rate %]
)
ORDER BY BI_Autotask_Tickets[created_month] ASCThe risk matrix shows that most entities fall in the low-risk category, but the high-risk group demands immediate attention. The moderate-risk group shows a declining trend that could escalate without intervention.
| Category | Items | Primary | Secondary | Status |
|---|---|---|---|---|
| Category A | 234 | 94.2% | 14 | Healthy |
| Category B | 187 | 89.3% | 20 | Review |
| Category C | 156 | 91.7% | 13 | Healthy |
| Category D | 98 | 86.7% | 13 | Review |
| Category E | 67 | 82.1% | 12 | At Risk |
| Category F | 45 | 95.6% | 2 | Healthy |
The detailed breakdown shows clear performance differences. The bottom two categories require targeted action to improve overall portfolio health.
Overall portfolio health is strong at 92.4%, but the 87.3% coverage rate suggests that roughly 1 in 8 entities is not fully monitored. The 23 open action items represent a manageable backlog if addressed within 2 weeks.
The gap between top and bottom performers is wider than expected. The bottom 20% scores more than 25 percentage points below the portfolio average, indicating structural issues that require targeted intervention.
Entities in the moderate risk category show a declining trend over the past quarter. Without intervention, 3-4 of these entities may shift to the high-risk category within 60 days.
The top 30% of the portfolio maintains stable performance above target, indicating current best practices are effective and can serve as a model for the rest.
1. Conduct a targeted review of all high-risk entities within 2 weeks. Document the root cause for each entity and create a remediation plan with clear deadlines and accountable owners.
2. Implement automated monitoring for the moderate-risk group. Set thresholds that trigger an alert when performance drops 5 percentage points below target, enabling early intervention before entities slip into high risk.
3. Schedule this report monthly as part of the QBR process. Use the trend data to verify that improvement initiatives are delivering measurable results across multiple quarters.
Data syncs every 24 hours from the source systems. The report reflects the most recent complete dataset.
Yes. This report is designed to be QBR-ready. Export the key metrics and trend data to include in your quarterly business review.
Schedule a targeted review for each high-risk entity. Create an action plan with remediation steps and follow up within 2 weeks.
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.
See more reports Get started