Lifecycle stage analysis of 9,207 managed assets, from deployment through maturity.
Lifecycle stage analysis of 9,207 managed assets, from deployment through maturity.
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
Lifecycle stage analysis of 9,207 managed assets, from deployment through maturity.
Key lifecycle metrics across the entire managed asset fleet.
EVALUATE TOPN(10, SUMMARIZECOLUMNS('BI_Autotask_Configuration_Items'[configuration_item_type_name], "Count", COUNTROWS('BI_Autotask_Configuration_Items'), "Active", CALCULATE(COUNTROWS('BI_Autotask_Configuration_Items'), 'BI_Autotask_Configuration_Items'[is_active] = TRUE())), [Count], DESC)Every active asset classified into six stages based on time since first tracked in Autotask.
| Lifecycle Stage | Assets | Share | Distribution |
|---|---|---|---|
| New (0-90 days) | 428 | 4.6% | |
| Recently Deployed (91-180 days) | 1,153 | 12.5% | |
| Mid-Life (181-365 days) | 2,013 | 21.9% | |
| Mature (1-2 years) | 5,613 | 61.0% | |
| Aging (2-3 years) | 0 | 0.0% | |
| Legacy (3+ years) | 0 | 0.0% |
EVALUATE
ROW(
"New_0_90", CALCULATE(
COUNTROWS('BI_Autotask_Configuration_Items'),
'BI_Autotask_Configuration_Items'[is_active] = TRUE(),
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) <= 90
),
"RecentlyDeployed_91_180", CALCULATE(
COUNTROWS('BI_Autotask_Configuration_Items'),
'BI_Autotask_Configuration_Items'[is_active] = TRUE(),
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) > 90 &&
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) <= 180
),
"MidLife_181_365", CALCULATE(
COUNTROWS('BI_Autotask_Configuration_Items'),
'BI_Autotask_Configuration_Items'[is_active] = TRUE(),
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) > 180 &&
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) <= 365
),
"Mature_366_730", CALCULATE(
COUNTROWS('BI_Autotask_Configuration_Items'),
'BI_Autotask_Configuration_Items'[is_active] = TRUE(),
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) > 365 &&
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) <= 730
),
"Aging_731_1095", CALCULATE(
COUNTROWS('BI_Autotask_Configuration_Items'),
'BI_Autotask_Configuration_Items'[is_active] = TRUE(),
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) > 730 &&
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) <= 1095
),
"Legacy_1096plus", CALCULATE(
COUNTROWS('BI_Autotask_Configuration_Items'),
'BI_Autotask_Configuration_Items'[is_active] = TRUE(),
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
) > 1095
)
)Average age per device type. Laptops average 1.4 years, while (Unclassified)s are the freshest at 0.4 years.
| Device Type | Count | Avg. Age | Years | Stage |
|---|---|---|---|---|
| Workstation | 5,024 | 464 days | 1.3 yrs | Mature |
| Laptop | 1,348 | 494 days | 1.4 yrs | Mature |
| Domain Registration | 907 | 410 days | 1.1 yrs | Mature |
| Mobile Device | 475 | 165 days | 0.5 yrs | Recently Deployed |
| Server | 378 | 417 days | 1.1 yrs | Mature |
| Access Point | 359 | 170 days | 0.5 yrs | Recently Deployed |
| (Unclassified) | 146 | 160 days | 0.4 yrs | Recently Deployed |
| Switch | 143 | 225 days | 0.6 yrs | Mid-Life |
| Firewall | 96 | 268 days | 0.7 yrs | Mid-Life |
| Addigy Device | 93 | 199 days | 0.5 yrs | Mid-Life |
| Printer | 79 | 228 days | 0.6 yrs | Mid-Life |
| Virtual Machine | 77 | 399 days | 1.1 yrs | Mature |
| Docking Station | 20 | 292 days | 0.8 yrs | Mid-Life |
| NAS Storage | 18 | 229 days | 0.6 yrs | Mid-Life |
| Monitor | 15 | 450 days | 1.2 yrs | Mature |
| Conference Setup | 11 | 90 days | 0.2 yrs | New |
| Cloud Service | 7 | 424 days | 1.2 yrs | Mature |
| Azure AVD | 5 | 469 days | 1.3 yrs | Mature |
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Configuration_Items'[configuration_item_type_name],
FILTER(
ALL('BI_Autotask_Configuration_Items'),
'BI_Autotask_Configuration_Items'[is_active] = TRUE()
),
"Count",
COUNTROWS('BI_Autotask_Configuration_Items'),
"AvgAgeDays",
AVERAGEX(
'BI_Autotask_Configuration_Items',
DATEDIFF(
'BI_Autotask_Configuration_Items'[create_date],
TODAY(), DAY
)
)
)
ORDER BY [Count] DESCConfiguration item categories with their average age and lifecycle stage classification.
| Category | Assets | Share | Avg. Age | Stage |
|---|---|---|---|---|
| Managed Endpoint | 6,408 | 69.6% | 471 days | Mature |
| Network Infrastructure | 904 | 9.8% | 411 days | Mature |
| Mobile & Portable | 475 | 5.2% | 165 days | Recently Deployed |
| Server Infrastructure | 428 | 4.6% | 175 days | Recently Deployed |
| Domain & Web Services | 383 | 4.2% | 421 days | Mature |
| Peripheral & Accessory | 380 | 4.1% | 170 days | Recently Deployed |
| Apple Managed | 93 | 1.0% | 199 days | Mid-Life |
| Virtualization | 89 | 1.0% | 381 days | Mature |
| AV & Conference | 29 | 0.3% | 336 days | Mid-Life |
| Storage & Backup | 13 | 0.1% | 393 days | Mature |
| Cloud Infrastructure | 5 | 0.1% | 386 days | Mature |
Top 15 clients ranked by asset count, with average fleet age and refresh rate (assets under 6 months old).
| Client | Assets | Avg. Age | Stage | New (< 6mo) | Refresh Rate |
|---|---|---|---|---|---|
| Whitfield Group | 1,554 | 497 days | Mature | 174 | 11.2% |
| Anderson & Partners | 1,367 | 354 days | Mid-Life | 495 | 36.2% |
| Mitchell Holdings | 466 | 295 days | Mid-Life | 24 | 5.2% |
| Reynolds Corp | 425 | 443 days | Mature | 57 | 13.4% |
| Wall PLC | 321 | 538 days | Mature | 11 | 3.4% |
| Price-Gomez | 241 | 346 days | Mid-Life | 114 | 47.3% |
| Carter Technologies | 230 | 400 days | Mature | 24 | 10.4% |
| Sullivan, Contreras and Rios | 174 | 500 days | Mature | 8 | 4.6% |
| Richards, Morgan and Blake | 172 | 312 days | Mid-Life | 67 | 39.0% |
| Leach, Patterson and Cole | 158 | 229 days | Mid-Life | 21 | 13.3% |
| Thompson Ventures | 149 | 453 days | Mature | 16 | 10.7% |
| Crawford, Kelly and Brooks | 145 | 415 days | Mature | 23 | 15.9% |
| Rivers, Hudson and Grant | 139 | 405 days | Mature | 18 | 12.9% |
| Bennett Associates | 137 | 325 days | Mid-Life | 76 | 55.5% |
| Harrison Dynamics | 129 | 334 days | Mid-Life | 17 | 13.2% |
Comparing the active fleet to retired assets reveals turnover patterns and refresh cadence.
Decommissioned assets averaged 1.3 years before being retired, compared to the current active fleet average of 1.1 years. The turnover ratio of 49.5% indicates that for every two active assets, roughly one has been decommissioned. This is a normal pattern for MSPs with ongoing hardware refresh cycles.
The managed fleet of 9,207 active assets sits at an average age of 1.1 years (417 days). The distribution is weighted toward the "Mature" stage: 5,613 assets (61.0%) fall in the 1-2 year bracket. This is expected for an MSP running regular hardware deployment cycles.
1,581 assets (17.2%) were deployed in the last six months, indicating active onboarding and refresh activity. The "New" bucket (under 90 days) holds 428 assets, while "Recently Deployed" (91-180 days) adds another 1,153.
By device type, laptops carry the highest average age at 1.4 years, followed by workstations at 1.3 years. Mobile devices and access points are the freshest, both under 6 months old on average. This pattern suggests endpoint refresh cycles run about every 12-18 months, while network gear gets replaced more frequently.
At the client level, Wall PLC has the oldest fleet at 1.5 years average, with only 11 new assets in the last six months. Meanwhile, Bennett Associates shows the highest refresh rate at 55.5% of their fleet being under 6 months old. That gap points to an opportunity for proactive refresh conversations with aging-fleet clients.
The decommissioned pool of 4,562 assets had an average age of 1.3 years at retirement, slightly higher than the current active fleet average. The 49.5% turnover ratio is healthy for an MSP operation and confirms that hardware is being cycled out before reaching end-of-life risk.
Wall PLC and Wall PLC both have fleets averaging over 1.5 years. Bring lifecycle data to their next QBR and start the refresh planning conversation before assets age into the risk zone.
Workstations and laptops average 1.3-1.4 years, while access points and mobile devices sit under 6 months. These need different refresh cycle targets. Set a 3-year ceiling for endpoints and 5 years for network infrastructure.
Older assets generate more support tickets and carry higher failure risk. Consider a tiered pricing model where devices past 3 years attract a small surcharge to fund proactive replacement budgets.
Use the DAX queries in this report as the basis for a monthly lifecycle dashboard. Flag any client whose average fleet age crosses the 2-year mark and route it to the account manager for review.
4,562 decommissioned assets remain in the system. Quarterly cleanup keeps the data accurate, prevents accidental billing on retired hardware, and reduces noise in fleet reports.
Each asset is classified based on how many days have passed since its create_date in Autotask PSA. The stages are: New (0-90 days), Recently Deployed (91-180 days), Mid-Life (181-365 days), Mature (1-2 years), Aging (2-3 years), and Legacy (3+ years). These thresholds align with typical MSP hardware refresh cycles.
The data model includes warranty_expiration_date fields, but in this demo dataset those fields are not populated. In a live environment, warranty dates would add another layer of lifecycle tracking: you could flag assets where the warranty has expired but the device is still active.
In Autotask PSA, an inactive configuration item is one that has been marked as no longer in service. This report treats "inactive" and "decommissioned" as the same status. Some MSPs use inactive to mean temporarily offline, but for lifecycle analysis purposes, any asset marked inactive is considered out of the active fleet.
Look at Section 5.0 (Client Fleet Age). Clients with an average fleet age above 1.3 years and a low refresh rate (under 10% new assets) are prime candidates for a refresh conversation. Bring this data to your QBR meetings.
Yes. The DAX queries in this report use DATEDIFF with configurable day ranges. Adjust the thresholds (90, 180, 365, 730, 1095 days) to match your organization's refresh policies. For example, if you use a 4-year refresh cycle, change the "Aging" bracket to 1095-1460 days.
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