Which ticket categories keep coming back, how much time they consume, and where automation or root cause fixes would have the highest impact. Generated by AI via Proxuma Power BI MCP server.
Which ticket categories keep coming back, how much time they consume, and where automation or root cause fixes would have the highest impact. 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 ticket categories keep coming back, how much time they consume, and where automation or root cause fixes would have the highest impact. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"TotalTickets", COUNTROWS('BI_Autotask_Tickets'),
"TopCategoryVolume", MAXX(
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[ticket_category_name]),
"cnt", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
), [cnt]),
"CategoryCount", DISTINCTCOUNT('BI_Autotask_Tickets'[ticket_category_name]),
"AvgHours", AVERAGE('BI_Autotask_Tickets'[worked_hours])
)All ticket categories ranked from highest to lowest volume, with average hours per ticket and share of total
| Category | Issue Type | Count |
|---|---|---|
| Airline pilot | General practice doctor | 13,313 |
| Research scientist | Education officer, community | 11,458 |
| Product manager | Therapist, speech and language | 9,697 |
| Research scientist | Public librarian | 6,062 |
| Product manager | Financial risk analyst | 4,026 |
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Tickets'[ticket_category_name], 'BI_Autotask_Tickets'[issue_type_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets')), [TicketCount], DESC)Issue types and sub-issue types ranked by ticket volume, showing where the repeat work concentrates at a more granular level
| # | Issue Type | Tickets | Avg Hours | Volume Level |
|---|---|---|---|---|
| 1 | General practice doctor | 15,835 | 0.45h | Very High |
| 2 | Development officer | 11,757 | 0.78h | Very High |
| 3 | Therapist | 9,866 | 0.68h | High |
| 4 | Public librarian | 6,117 | 0.71h | High |
| # | Sub-Issue Type | Tickets | Pattern |
|---|---|---|---|
| 1 | Furniture conservator | 4,318 | Recurring |
| 2 | Teacher, secondary school | 3,338 | Recurring |
| 3 | Insurance claims handler | 3,204 | Recurring |
-- Issue types by volume
EVALUATE
TOPN(
10,
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[issue_type_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"avg_hours", CALCULATE(AVERAGE('BI_Autotask_Tickets'[worked_hours]))
),
[tickets], DESC
)
-- Ticket type distribution
EVALUATE
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[ticket_type_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))
)
ORDER BY [tickets] DESCTotal engineer hours consumed by the highest-volume categories, showing the real operational cost of repeat tickets
| Category | Tickets | Avg Hours | Total Hours | Hours Impact |
|---|---|---|---|---|
| Research scientist (life sciences) | 27,955 | 0.81h | 22,644h | Highest |
| Product manager | 16,578 | 0.88h | 14,589h | Very High |
| Airline pilot | 13,316 | 0.43h | 5,726h | High |
EVALUATE
TOPN(
10,
ADDCOLUMNS(
VALUES('BI_Autotask_Tickets'[ticket_category_name]),
"tickets", CALCULATE(COUNTROWS('BI_Autotask_Tickets')),
"avg_hours", CALCULATE(AVERAGE('BI_Autotask_Tickets'[worked_hours])),
"total_hours", CALCULATE(SUM('BI_Autotask_Tickets'[worked_hours]))
),
[total_hours], DESC
)One category dominates: Research scientist (life sciences) accounts for 41.4% of all tickets at 27,955 entries. That is not a busy category. That is a structural pattern. With an average of 0.81 hours per ticket, these are not complex issues either. They are repetitive, predictable, and exactly the kind of work that should be reduced through better tooling or self-service options.
The top three categories together make up 85.7% of all ticket volume. Everything else is noise by comparison. If you want to move the needle on service desk efficiency, these three categories are where to focus.
Airline pilot stands out for a different reason. It has the third-highest volume (13,316 tickets) but the lowest average hours (0.43h). These tickets are being resolved quickly, which suggests they may be alerts or automated entries rather than real user requests. A large portion of these are likely candidates for auto-resolution or suppression.
On the other end, Local government officer and Accountant, chartered have relatively low volume but high average hours (2.62h and 2.18h respectively). These are the expensive tickets. Each one takes more than twice the time of the average ticket. Reducing volume in these categories would free up significant engineer time even though they represent only 4.5% of total tickets.
The ticket type breakdown shows that incidents make up 41% and alerts 29%. The alert volume is worth investigating. Nearly 20,000 alert tickets suggests either noisy monitoring or a threshold configuration that needs tuning. Every unnecessary alert consumes triage time.
5 priorities based on the findings above
Research scientist (life sciences) generates 27,955 tickets at 0.81h each. Pull a sample of 50 recent tickets in this category and look for patterns. If 60% or more follow the same resolution steps, build a runbook or script for it. Even partial automation at this volume saves thousands of hours.
19,790 alert tickets is a red flag. Review your RMM and monitoring tool configurations. Identify which alerts are actionable versus informational. Suppressing or auto-closing alerts that resolve themselves could remove thousands of tickets from your queue without any risk to service quality.
With 13,316 tickets and only 0.43h average resolution, these are quick-fix issues. A knowledge base article, a guided troubleshooting flow, or a self-service portal entry could deflect a meaningful percentage of these. The goal is not to eliminate them, but to shift 20-30% to self-service.
Local government officer (2.62h avg) and Accountant, chartered (2.18h avg) are consuming disproportionate time. These 3,067 tickets together account for roughly 7,438 engineer hours. Look for recurring sub-issues within these categories. A single root cause fix here could reduce average resolution time by 30-50%.
Set up a recurring report or Power BI dashboard page that shows the top 10 categories by volume each month. After implementing automation or root cause fixes, you should see the volume and hours trend downward. If a category stays flat, the fix did not work and needs revisiting.
All ticket data comes from Autotask PSA via the Proxuma Power BI connector. The semantic model includes ticket categories, issue types, sub-issue types, worked hours, and ticket types. The AI queries this model using DAX and groups the results by category to identify recurring patterns.
This report groups tickets by category and issue type, then ranks by volume. A "recurring" pattern is any category that appears with high frequency. The report does not track individual ticket re-opens. Instead, it surfaces categories where the same type of work keeps appearing, which is a stronger signal of a systemic issue.
This demo report uses synthetic data generated by a faker library. In production, these category names would be your actual Autotask ticket categories like "Password Reset", "Email Issue", "VPN Connectivity", or whatever your PSA is configured to track.
Three approaches work: (1) Automation via scripting or RMM policies for tickets with predictable resolution steps. (2) Self-service portals or knowledge base articles for user-fixable issues. (3) Root cause analysis for categories where a single infrastructure or configuration fix eliminates the trigger entirely.
Yes. Add a filter to the DAX queries for BI_Autotask_Tickets[company_name] to see recurring patterns per client, or filter on BI_Autotask_Tickets[create_date] to limit to a specific time range. Per-client analysis is useful for QBRs and identifying client-specific issues.
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 real data, and produces a report like this in under fifteen minutes.
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.
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