Team (TISI) Operating Expenses (2010 - 2026)
Team (TISI) recorded Operating Expenses of $51.25 million in Q2 2026, down 7.3% from $55.27 million a year earlier and down 2.5% from the prior quarter.
Powered by 
Analysis
Team (TISI) Operating Expenses (2010 - 2026) Analysis & Trends
On a TTM basis, Team's Operating Expenses came in at $212.2 million as of Jun 30, 2026, down 0.6% year-over-year; for FY2025, it came in at $217.64 million, up 2.2% from FY2024.
- Annual Operating Expenses has a five-year compound annual growth rate of -3.8% (FY2020 to FY2025).
- Across earlier years, Operating Expenses came in at $213.01 million in FY2024 (-5.1%), $224.43 million in FY2023 (-7.0%), $241.41 million in FY2022 (-2.9%) and $248.74 million in FY2021 (-5.9%).
- The Q2 2026 figure is the lowest quarterly Operating Expenses since Q3 2024.
- On a year-over-year basis, Operating Expenses has declined for three consecutive quarters, with an average decline of 1.7% over the last eight quarters.
- Peak year-over-year performance for Operating Expenses in the last five years was growth of 30.1% in Q4 2022, against a decline of 29.7% in Q4 2021 at the low end.
- Per Business Quant, the preceding three quarters came in at $52.56 million (Q1 2026), $51.68 million (Q4 2025) and $56.7 million (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Accenture | 146.60 Bn | 105.89 Bn | 6.13 Bn | 15.54 Bn |
| 2 | Cintas | 85.99 Bn | 85.07 Bn | 1.55 Bn | 841.66 Mn |
| 3 | Iron Mountain | 33.66 Bn | 32.85 Bn | 1.07 Bn | 1.66 Bn |
| 4 | APi | 17.29 Bn | 14.32 Bn | 703.00 Mn | 528.00 Mn |
| 5 | Rollins | 15.91 Bn | 15.46 Bn | 569.95 Mn | 877.22 Mn |
| 6 | UL Solutions | 14.79 Bn | 13.55 Bn | 417.00 Mn | 267.00 Mn |
| 7 | Aramark | 14.76 Bn | 12.71 Bn | 430.34 Mn | 4.84 Bn |
| 8 | Gartner | 14.54 Bn | 8.23 Bn | 1.19 Bn | 1.30 Bn |
| 9 | Rentokil Initial | 10.68 Bn | 4.19 Bn | - | - |
| 10 | Team | 111.88 Mn | 40.10 Mn | 54.40 Mn | 51.25 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 51.25 Mn |
| Mar 31, 2026 | 52.56 Mn |
| Dec 31, 2025 | 51.68 Mn |
| Sep 30, 2025 | 56.70 Mn |
| Jun 30, 2025 | 55.27 Mn |
| Mar 31, 2025 | 52.67 Mn |
| Dec 31, 2024 | 55.14 Mn |
| Sep 30, 2024 | 50.37 Mn |
| Jun 30, 2024 | 52.40 Mn |
| Mar 31, 2024 | 55.12 Mn |
| Dec 31, 2023 | 59.32 Mn |
| Sep 30, 2023 | 54.05 Mn |
| Jun 30, 2023 | 56.32 Mn |
| Mar 31, 2023 | 54.75 Mn |
| Dec 31, 2022 | 57.22 Mn |
| Sep 30, 2022 | 57.75 Mn |
| Jun 30, 2022 | 62.91 Mn |
| Mar 31, 2022 | 63.54 Mn |
| Dec 31, 2021 | 43.97 Mn |
| Sep 30, 2021 | 68.01 Mn |
API Access
Team Operating Expenses API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=operating-expenses&ticker=TISI&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "TISI", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=operating-expenses&ticker=TISI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();