What to cover, what the numbers say, and what to prioritize for the next quarter. Generated by AI via Proxuma Power BI MCP server.
What to cover, what the numbers say, and what to prioritize for the next quarter. 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: Account managers, MSP owners, and vCTOs preparing executive reviews
How often: Quarterly for scheduled QBRs, on-demand for executive briefings
What to cover, what the numbers say, and what to prioritize for the next quarter. Generated by AI via Proxuma Power BI MCP server.
EVALUATE ROW("TotalTickets", COUNTROWS('BI_Autotask_Tickets'), "ResolutionSLA", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[resolution_met] + 0 = 1), "AvgResolution", AVERAGE('BI_Autotask_Tickets'[resolution_duration_hours]), "TotalProjects", COUNTROWS('BI_Autotask_Projects'), "TotalDevices", COUNTROWS('BI_Datto_Rmm_Devices'))A structured meeting agenda with the data behind every talking point. Present these items in order. Each one links to a detailed section below.
Ticket volume, SLA compliance, and open backlog from Autotask PSA
| Metric | Value | Status |
|---|---|---|
| Total Tickets | 67,521 | Volume |
| Open Tickets | ~900 | Backlog |
| SLA First Response Met | 52.9% | Below Target |
| SLA Resolution Met | 63.5% | Below Target |
| P1 First Response Met | 68.6% | Acceptable |
| P1 Resolution Met | 71.8% | Acceptable |
| P1 Ticket Count | 1,788 | Volume |
| Hours Worked | 50,752 | Volume |
| Billable Hours | 38,368 | 75.6% |
EVALUATE ROW(
"TotalRevenue", [Revenue - Total],
"TotalCost", [Cost - Total],
"TotalHours", [Company - Hours Worked],
"TotalBillable", [Company - Billable Hours],
"TotalTickets", COUNTROWS(BI_Autotask_Tickets),
"FRMet", [Tickets - First Response Met %],
"ResMet", [Tickets - Resolution Met %]
)Managed device inventory and online/offline ratio from Datto RMM
| Metric | Count | Percentage | Status |
|---|---|---|---|
| Total Managed Devices | 7,080 | 100% | Baseline |
| Online Devices | 978 | 13.8% | Critical |
| Offline Devices | 6,105 | 86.2% | Critical |
An 86.2% offline rate across 7,080 devices is the single most important infrastructure metric in this review. Before raising alarm, determine how many of those 6,105 offline devices are genuinely unreachable vs. decommissioned machines with agents that were never removed. A stale device inventory inflates risk perception and makes alert data unreliable. The cleanup itself is a billable project: audit, remove stale agents, and establish a decommission process going forward.
EVALUATE SUMMARIZECOLUMNS(
BI_Datto_Rmm_Accounts[Name],
"ManagedDevices", SUM(BI_Datto_Rmm_Accounts[Number_Of_Managed_Devices]),
"OnlineDevices", SUM(BI_Datto_Rmm_Accounts[Number_Of_Online_Devices]),
"OfflineDevices", SUM(BI_Datto_Rmm_Accounts[Number_Of_Offline_Devices])
)Alert analysis from Datto RMM, focusing on unresolved critical and high-priority items
| Priority | Resolved | Open | Total | Resolution Rate |
|---|---|---|---|---|
| Critical | 3,737 | 49 | 3,786 | 98.7% |
| High | 1,397 | 70 | 1,467 | 95.2% |
| All Priorities | 131,818 | 3,369 | 135,387 | 97.5% |
49 open critical alerts and 70 open high alerts represent active, unresolved risk. The overall resolution rate of 97.5% is strong, but that still leaves 3,369 alerts unaddressed. In a quarterly review, focus the conversation on the 119 critical+high open items. Pull the specific alert types, identify which devices they sit on, and agree on a 30-day remediation window. The 6,105 offline devices compound this problem: you cannot resolve alerts on machines you cannot reach.
EVALUATE SUMMARIZECOLUMNS(
BI_Datto_Rmm_Alerts[priority],
BI_Datto_Rmm_Alerts[resolved],
"AlertCount", COUNTROWS(BI_Datto_Rmm_Alerts)
)Revenue, cost, margin, and utilization from Autotask PSA
| Metric | Value | Notes |
|---|---|---|
| Total Hours Worked | 50,752 | All tracked time entries |
| Billable Hours | 38,368 | 75.6% of total |
| Non-Billable Hours | 12,384 | 24.4% internal + admin |
| Revenue per Hour | €347 | EUR 17.6M / 50,752 hrs |
| Cost per Hour | €164 | EUR 8.3M / 50,752 hrs |
A 53% margin is healthy for an MSP, but the 75.6% billable rate leaves room for improvement. Each percentage point of billable time recovered translates to roughly 508 additional billable hours per year. At the current revenue-per-hour rate, that is approximately EUR 176K in annual revenue per point. The quarterly review should identify where non-billable hours are being spent and whether any of that work can be scoped into client contracts.
EVALUATE ROW(
"TotalRevenue", [Revenue - Total],
"TotalCost", [Cost - Total],
"TotalHours", [Company - Hours Worked],
"TotalBillable", [Company - Billable Hours],
"Margin", DIVIDE([Revenue - Total] - [Cost - Total], [Revenue - Total])
)Data-driven priorities for the next quarter, ordered by urgency
86.2% of managed devices are offline. That number makes every other RMM metric unreliable. Schedule a device audit: identify decommissioned machines, remove stale agents, and flag genuinely unreachable endpoints for investigation. Set a target of under 30% offline by end of next quarter. This is also a billable project you can scope for the client.
49 critical and 70 high-priority alerts are currently unresolved. These represent active security and operational risk. Pull the specific alert categories, assign owners, and set a 30-day closure deadline. Any alert that cannot be resolved due to an offline device becomes part of the device audit above.
A 52.9% first response SLA is a contractual liability. Investigate whether the issue is staffing (not enough hands during peak hours), process (tickets sitting in queues without assignment), or scope (SLA timers set too aggressively for the current team size). A 10-point improvement to 63% would bring first response closer to the resolution rate and show a credible upward trend at the next QBR.
12,384 non-billable hours this period represent lost revenue. Audit the top non-billable time categories: internal meetings, unbilled client work, and administrative tasks. If even half of that time can be shifted into billable categories or scoped into contracts, the revenue impact is significant. Each point of utilization improvement is worth approximately EUR 176K annually.
35 billable M365 users is a small footprint. Confirm this matches the client's current employee count. Look for unused licenses, shared mailboxes on paid seats, and users without MFA enabled. A quick M365 hygiene check takes 15 minutes and often surfaces upsell opportunities for security add-ons or license tier upgrades.
Three sources: Autotask PSA (tickets, hours, SLAs, financials), Datto RMM (devices, alerts, online/offline status), and Microsoft 365 via N-able (billable user count). Proxuma Power BI connects to all three through pre-built connectors and normalizes the data into a single semantic model. The AI runs DAX queries against that model to produce this report.
Quarterly for the full agenda. The service desk and infrastructure sections (3.0 and 4.0) are worth reviewing monthly if SLA performance is below target or if you are in the middle of a device cleanup. The financial section is most useful on a quarterly or annual cadence when evaluating contract renewals or pricing adjustments.
A high offline rate usually means one of three things: decommissioned devices with agents that were never removed, seasonal or part-time machines that are turned off, or genuinely unreachable endpoints with connectivity issues. The fix is to audit the device list, remove stale entries, and investigate the remaining offline machines. Most MSPs find that 40-60% of their offline devices are stale agents.
Most MSP contracts target 80% or higher for first response SLA. Top-performing MSPs achieve 85-90%. A rate below 60% typically indicates a staffing or process issue rather than a tooling problem. The most common fixes are automated ticket assignment, dedicated triage during peak hours, and adjusting SLA timers to match realistic response capacities.
Yes. Connect Proxuma Power BI to your Autotask PSA, Datto RMM, and M365 environments, 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. Every DAX query in this report is executable against the standard Proxuma semantic model.
That depends on the MSP. Some MSPs share a simplified version showing hours worked and utilization, while keeping revenue, cost, and margin internal. If you share financials, focus on the client-specific numbers (hours spent on their account, SLA performance) rather than portfolio-wide revenue. The Proxuma anonymization layer can filter financial data per client if needed.
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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