Comparing change request volume to reactive work across all managed clients. Generated by AI via Proxuma Power BI MCP server.
Comparing change request volume to reactive work across all managed clients. 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: MSP operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
Comparing change request volume to reactive work across all managed clients. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "ServiceChangeTickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[priority_name] = "Service/Change req."))Of 67,521 total tickets, only 7,247 (10.7%) are change requests. The remaining 60,274 tickets (89.3%) are reactive: incidents, alerts, service requests, and problems.
| Ticket Type | Count | % of Total | Category | Share |
|---|---|---|---|---|
| Incident | 27,664 | 41.0% | Reactive | |
| Alert | 19,790 | 29.3% | Reactive | |
| Service Request | 12,653 | 18.7% | Reactive | |
| Change Request | 7,247 | 10.7% | Standard | |
| Problem | 167 | 0.2% | Reactive |
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[ticket_type_name],
"ticket_count", COUNTROWS('BI_Autotask_Tickets')
)
ORDER BY [ticket_count] DESCTop 10 clients ranked by change request volume, with total hours logged and average hours per CR
| # | Company | CR Count | Total Hours | Avg Hours/CR | Volume |
|---|---|---|---|---|---|
| 1 | Martin Group | 696 | 770.7h | 1.24h | |
| 2 | Foster Inc | 520 | 419.2h | 1.00h | |
| 3 | Patterson Hood Perez | 500 | 416.0h | 0.94h | |
| 4 | Wall PLC | 436 | 337.2h | 0.80h | |
| 5 | Hernandez Ltd | 336 | 588.0h | 1.92h | |
| 6 | Nelson Taylor Hicks | 280 | 177.5h | 0.68h | |
| 7 | Rivers Rogers Mitchell | 244 | 156.0h | 0.90h | |
| 8 | Martinez Contreras Rios | 243 | 236.9h | 1.22h | |
| 9 | Price-Gomez | 236 | 168.4h | 0.74h | |
| 10 | Holt Bradley Fowler | 216 | 125.7h | 0.61h |
EVALUATE
TOPN(30,
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[company_name],
FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[ticket_type_name] = "Change Request"),
"ticket_count", COUNTROWS('BI_Autotask_Tickets'),
"total_worked_hours", SUM('BI_Autotask_Tickets'[worked_hours]),
"avg_worked_hours", AVERAGE('BI_Autotask_Tickets'[worked_hours])
),
[ticket_count], DESC
)Average hours per change request by company. Lower is faster. The spread between the fastest (0.61h) and slowest (1.92h) is a 3x difference.
EVALUATE
TOPN(30,
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[company_name],
FILTER('BI_Autotask_Tickets', 'BI_Autotask_Tickets'[ticket_type_name] = "Change Request"),
"ticket_count", COUNTROWS('BI_Autotask_Tickets'),
"total_worked_hours", SUM('BI_Autotask_Tickets'[worked_hours]),
"avg_worked_hours", AVERAGE('BI_Autotask_Tickets'[worked_hours])
),
[ticket_count], DESC
)Incidents and alerts together make up 70.3% of all tickets (47,454 of 67,521). This means the service desk spends the vast majority of its capacity responding to things that have already gone wrong. A 10.7% standard change ratio is roughly half of what mature IT operations typically aim for. The industry benchmark for managed services sits between 15% and 25%.
Most companies average under 1 hour per CR. Hernandez Ltd takes nearly double that, logging 588 hours across 336 change requests. This could indicate poorly scoped requests, an environment that requires more manual steps, or a documentation gap. Either way, it is the single largest efficiency opportunity in the CR data.
Wall PLC (0.80h), Rivers Rogers Mitchell (0.90h), Patterson Hood Perez (0.94h), Price-Gomez (0.74h), Nelson Taylor Hicks (0.68h), and Holt Bradley Fowler (0.61h) all complete change requests quickly. These accounts likely have well-documented environments, repeatable change templates, or smaller scopes. Their processes are worth studying and replicating for slower accounts.
4 priorities based on the findings above
Pull the last 30 change requests for Hernandez Ltd and review the time entries. Look for patterns: are certain CR categories consistently taking longer? Is there a single technician logging most of the hours? A 1.92h average is 83% above the portfolio average of 1.05h. If you can bring their average down to 1.2h, you save roughly 242 hours annually on their CRs alone.
With 27,664 incidents in the dataset, there are likely repeating patterns: the same fix applied to the same problem type across multiple clients. Identify the top 10 most common incident categories and evaluate which ones can be prevented through a scheduled change or automation. Converting even 5% of incidents into proactive changes would add 1,383 tickets to the planned side and shift the ratio toward 12.7%.
Holt Bradley Fowler and Nelson Taylor Hicks both process CRs in under 0.70h on average. Document their most common change types, the steps involved, and the time entries. Build these into CR templates in Autotask so that technicians working on slower accounts can follow the same approach. Standardized CR templates reduce average handling time and improve consistency.
The current 10.7% is a starting point, not a ceiling. Track this metric monthly. As you convert common incidents to CRs and introduce more proactive maintenance windows, the ratio should climb. A target of 15% within six months is realistic and measurable. Beyond 20% puts you in the top quartile of MSPs for operational maturity.
Standard changes are tickets with the type "Change Request" in Autotask. These are pre-approved, planned work items: new user setups, scheduled migrations, hardware replacements, software deployments. They follow a defined scope and are created before the work begins, unlike incidents or alerts which are triggered by something breaking.
ITIL and MSP maturity frameworks generally recommend that 15% to 25% of service desk work should be planned changes. At 10.7%, nearly nine out of ten tickets are reactive. This means the team spends most of its time on unplanned work, which is harder to schedule, harder to scope, and more likely to result in overtime or missed SLAs.
For the purposes of this report, yes. Service requests are user-initiated and typically arrive without advance scheduling. While some (like password resets) are routine, they are not pre-approved changes with a defined scope. They hit the queue unscheduled, which makes them reactive from a resource planning perspective.
Three approaches work well: (1) identify the top 10 recurring incidents and convert them into proactive, scheduled maintenance changes, (2) introduce monthly or quarterly change windows per client for patching, updates, and hardware refreshes, and (3) build CR templates in Autotask so technicians can log planned work as change requests instead of miscategorizing them as service requests.
Yes. Connect Proxuma Power BI to your Autotask PSA, 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 live data, and produces a report like this in under fifteen minutes. Your numbers will be different, but the structure and analysis are the same.
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