AI-generated analysis of 262 client projects, completion timing, and task bottlenecks.
AI-generated analysis of 262 client projects, completion timing, and task bottlenecks.
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: Account managers, MSP owners, and service delivery leads
How often: Monthly for client reviews, quarterly for QBRs, on-demand when client signals change
AI-generated analysis of 262 client projects, completion timing, and task bottlenecks.
Key performance indicators across 262 client onboarding projects.
EVALUATE ROW("TotalCompanies", COUNTROWS('BI_Autotask_Companies'), "ActiveCompanies", CALCULATE(COUNTROWS('BI_Autotask_Companies'), 'BI_Autotask_Companies'[status]), "WithContracts", DISTINCTCOUNT('BI_Autotask_Contracts'[company_id]), "ActiveContracts", CALCULATE(COUNTROWS('BI_Autotask_Contracts'), 'BI_Autotask_Contracts'[contract_status_name] = "Active"))Distribution of all client projects by their current status.
Of 262 client projects, 190 have reached completion. 39 projects remain active, while 7 are currently on hold. The overall completion rate sits at 72.5%, with 40.0% of completed projects finishing before their scheduled deadline.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Projects'[project_status_name],
FILTER('BI_Autotask_Projects',
'BI_Autotask_Projects'[project_type_name] = "Client"),
"Count", COUNTROWS('BI_Autotask_Projects'))How many completed projects met their scheduled deadline?
Only 40.0% of onboarding projects wrapped up before their planned end date. The remaining 60.0% ran past deadline, pointing to consistent scheduling or scoping issues that need attention.
EVALUATE
VAR CompletedOnTime = CALCULATE(COUNTROWS('BI_Autotask_Projects'),
'BI_Autotask_Projects'[project_type_name] = "Client",
'BI_Autotask_Projects'[project_status_name] = "Complete",
NOT(ISBLANK('BI_Autotask_Projects'[end_date])),
NOT(ISBLANK('BI_Autotask_Projects'[complete_date])),
'BI_Autotask_Projects'[complete_date] <= 'BI_Autotask_Projects'[end_date])
VAR CompletedLate = CALCULATE(COUNTROWS('BI_Autotask_Projects'),
'BI_Autotask_Projects'[project_type_name] = "Client",
'BI_Autotask_Projects'[project_status_name] = "Complete",
NOT(ISBLANK('BI_Autotask_Projects'[end_date])),
NOT(ISBLANK('BI_Autotask_Projects'[complete_date])),
'BI_Autotask_Projects'[complete_date] > 'BI_Autotask_Projects'[end_date])
VAR TotalWithDates = CompletedOnTime + CompletedLate
RETURN ROW("OnTime", CompletedOnTime, "Late", CompletedLate,
"Total", TotalWithDates, "OnTimeRate", DIVIDE(CompletedOnTime, TotalWithDates))How long do client onboarding projects take from start to completion?
The gap between median (98 days) and average (133 days) shows that a small group of projects drags the average up significantly. Most onboardings finish within about three months, but outliers can stretch past a year.
EVALUATE
VAR Completed = FILTER('BI_Autotask_Projects',
'BI_Autotask_Projects'[project_type_name] = "Client"
&& 'BI_Autotask_Projects'[project_status_name] = "Complete"
&& NOT(ISBLANK('BI_Autotask_Projects'[start_date]))
&& NOT(ISBLANK('BI_Autotask_Projects'[complete_date])))
VAR WithDur = ADDCOLUMNS(Completed, "Dur",
DATEDIFF('BI_Autotask_Projects'[start_date],
'BI_Autotask_Projects'[complete_date], DAY))
RETURN ROW(
"AvgDays", AVERAGEX(WithDur, [Dur]),
"MedianDays", MEDIANX(WithDur, [Dur]),
"MinDays", MINX(WithDur, [Dur]),
"MaxDays", MAXX(WithDur, [Dur]),
"Count", COUNTROWS(WithDur))Top 10 clients ranked by project volume with completion metrics.
| Client | Projects | Completed | Rate | Tasks |
|---|---|---|---|---|
| Anderson & Partners | 29 | 21 | 72.4% | 72 |
| Mitchell Group | 10 | 10 | 100.0% | 31 |
| Clarke, Bennett and Harris | 10 | 8 | 80.0% | 40 |
| Rivers, Parker and Cole | 10 | 4 | 40.0% | 76 |
| Wall PLC | 9 | 8 | 88.9% | 25 |
| Thompson Industries | 9 | 5 | 55.6% | 29 |
| Whitfield Corp | 8 | 8 | 100.0% | 31 |
| Reynolds, Foster and Smith | 6 | 4 | 66.7% | 12 |
| Davidson Holdings | 6 | 4 | 66.7% | 22 |
| Carter Solutions | 5 | 5 | 100.0% | 7 |
| Price-Gomez | 5 | 4 | 80.0% | 7 |
| Morgan Enterprises | 5 | 2 | 40.0% | 20 |
| Brooks Consulting | 5 | 2 | 40.0% | 10 |
Clients with higher project volumes tend to show completion rates close to the portfolio average. Three clients stand out with 100% completion rates, though they carry fewer projects overall. The clients with the lowest rates (40.0%) each have multiple projects still in progress.
EVALUATE
TOPN(10,
ADDCOLUMNS(
SUMMARIZE(
FILTER('BI_Autotask_Projects',
'BI_Autotask_Projects'[project_type_name] = "Client"),
'BI_Autotask_Projects'[company_name]),
"Total", CALCULATE(COUNTROWS('BI_Autotask_Projects')),
"Completed", CALCULATE(COUNTROWS('BI_Autotask_Projects'),
'BI_Autotask_Projects'[project_status_name] = "Complete"),
"Rate", DIVIDE(
CALCULATE(COUNTROWS('BI_Autotask_Projects'),
'BI_Autotask_Projects'[project_status_name] = "Complete"),
CALCULATE(COUNTROWS('BI_Autotask_Projects'))),
"Tasks", CALCULATE(COUNTROWS('BI_Autotask_Tasks'))),
[Total], DESC)Are onboarding tasks finishing within their estimated hours?
On average, tasks come in at 85.4% of their estimated hours. While 30.0% of tasks exceed their budgeted time, the portfolio-level average stays under estimate, meaning that faster tasks offset the overruns.
EVALUATE
VAR Done = FILTER('BI_Autotask_Tasks',
RELATED('BI_Autotask_Projects'[project_type_name]) = "Client"
&& 'BI_Autotask_Tasks'[status_name] = "Complete"
&& NOT(ISBLANK('BI_Autotask_Tasks'[proxuma_estimated_in_hours]))
&& 'BI_Autotask_Tasks'[proxuma_estimated_in_hours] > 0)
RETURN ROW(
"TotalTasks", COUNTROWS(Done),
"AvgEst", AVERAGEX(Done, 'BI_Autotask_Tasks'[proxuma_estimated_in_hours]),
"AvgAct", AVERAGEX(Done, 'BI_Autotask_Tasks'[proxuma_actual_in_hours]),
"OverBudget", COUNTROWS(FILTER(Done,
'BI_Autotask_Tasks'[proxuma_actual_in_hours] > 'BI_Autotask_Tasks'[proxuma_estimated_in_hours])),
"OverBudgetRate", DIVIDE(
COUNTROWS(FILTER(Done,
'BI_Autotask_Tasks'[proxuma_actual_in_hours] > 'BI_Autotask_Tasks'[proxuma_estimated_in_hours])),
COUNTROWS(Done)))Which incomplete task types block onboarding progress?
Installation requests and backup requests account for the largest share of outstanding tasks across active onboarding projects. These two categories together represent over 263 open items, making them the primary blockers for project completion.
EVALUATE
TOPN(8,
ADDCOLUMNS(
SUMMARIZE(
FILTER('BI_Autotask_Tasks',
RELATED('BI_Autotask_Projects'[project_type_name]) = "Client"
&& 'BI_Autotask_Tasks'[status_name] <> "Complete"),
'BI_Autotask_Tasks'[title]),
"Count", CALCULATE(COUNTROWS('BI_Autotask_Tasks'))),
[Count], DESC)The numbers reveal two separate problems. First, the 72.5% completion rate means roughly one in four client projects never reaches a "Complete" status. Some of those sit in "Waiting to start" or "New" and may simply be recent additions. But 7 projects on hold suggest genuine stalls.
Second, among the projects that do complete, 60.0% miss their deadline. The median duration of 98 days compared to an average of 133 days shows that a handful of long-running projects pull the mean upward. These outliers likely involve scope changes, resource constraints, or client-side delays.
Task-level data adds useful context. With 30.0% of tasks exceeding their estimates, the budgeting accuracy is reasonable but not great. Installation requests and backup requests dominate the open task queue, which means infrastructure setup remains the most common sticking point in getting clients fully operational.
Based on the data, these are the highest-impact areas to address.
With only 40.0% of projects finishing on time, review how end dates are set. Consider adding buffer time to project templates or breaking large onboardings into shorter phases with their own milestones.
Installation requests (157 open) and backup requests (106 open) are the top two blockers. Assign dedicated capacity to these task types or create standardized runbooks to speed them up.
7 projects are on hold. Audit each one to determine if they should be resumed, rescheduled, or formally closed. Stale projects distort completion metrics and tie up resources.
Several clients show 100% completion rates. Study what these onboardings have in common (scope, team size, communication cadence) and apply those patterns to underperforming accounts.
The completion rate divides the number of client projects with status "Complete" (190) by the total number of client projects (262), giving 72.5%. This includes all client-type projects regardless of start date.
A project is considered on time if its actual completion date falls on or before its planned end date in Autotask. Projects without both dates are excluded from the on-time calculation.
The completion rate (72.5%) measures whether projects eventually finish. The on-time rate (40.0%) measures whether they finish by their target date. Many projects do complete, but after their planned deadline.
Project and task data comes from Autotask PSA via the Proxuma data model in Power BI. The AI analysis layer connects through MCP to run DAX queries and generate this report automatically.
Yes. Connect Proxuma Power BI to your Autotask PSA instance, add an AI assistant via MCP, and ask the same question. The report structure and DAX queries adapt to your data automatically.
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