Automatic Data Processing (ADP) Operating Expenses (2009 - 2026)
Automatic Data Processing's Operating Expenses came in at $4.34 billion for fiscal Q4 2026 (quarter ended Jun 30, 2026), up 7.5% from $4.03 billion a year earlier and up 2.4% from the prior quarter.
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Analysis
Automatic Data Processing (ADP) Operating Expenses (2009 - 2026) Analysis & Trends
For FY2026 (ended Jun 30, 2026), Automatic Data Processing's Operating Expenses was $16.63 billion, up 6.6% from FY2025.
- Operating Expenses has increased in each of the last 13 fiscal years, with a five-year compound annual growth rate of 7.2% (FY2021 to FY2026).
- Going back by fiscal year, Operating Expenses was $15.6 billion in FY2025 (+6.8%), $14.62 billion in FY2024 (+6.2%), $13.76 billion in FY2023 (+7.7%) and $12.78 billion in FY2022 (+8.8%).
- The fiscal Q4 2026 figure represents the highest quarterly Operating Expenses in data going back to fiscal Q3 2009.
- Year-over-year, Operating Expenses has increased for 23 consecutive quarters, with growth averaging 6.7% over the last eight quarters.
- Over the past five years, the year-over-year growth in Operating Expenses ranged from 4.8% (fiscal Q3 2024) to 9.6% (fiscal Q1 2023).
- Business Quant data shows ADP's Operating Expenses at $4.23 billion (Q3 2026), $4.08 billion (Q2 2026) and $3.98 billion (Q1 2026) in the three fiscal quarters before Q4 2026.
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Opex (Qtr) |
|---|---|---|---|---|---|
| 1 | Palantir Technologies | 480.73 Bn | 449.68 Bn | 1.64 Bn | 726.59 Mn |
| 2 | Oracle | 399.91 Bn | 272.04 Bn | - | 12.62 Bn |
| 3 | Sap Se | 262.30 Bn | 190.08 Bn | 8.40 Bn | -8.41 Bn |
| 4 | Salesforce | 202.26 Bn | 158.13 Bn | 8.70 Bn | 6.37 Bn |
| 5 | ServiceNow | 153.84 Bn | 132.30 Bn | 2.82 Bn | 2.66 Bn |
| 6 | Automatic Data Processing | 114.84 Bn | 96.97 Bn | 2.51 Bn | 4.34 Bn |
| 7 | Intuit | 91.04 Bn | 70.38 Bn | 3.44 Bn | 3.88 Bn |
| 8 | Relx | 70.51 Bn | 69.60 Bn | - | - |
| 9 | Strategy | 53.24 Bn | 46.23 Bn | 81.55 Mn | 8.41 Bn |
| 10 | Thomson Reuters | 48.40 Bn | 45.18 Bn | - | -1.21 Bn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 4.34 Bn |
| Mar 31, 2026 | 4.23 Bn |
| Dec 31, 2025 | 4.08 Bn |
| Sep 30, 2025 | 3.98 Bn |
| Jun 30, 2025 | 4.03 Bn |
| Mar 31, 2025 | 3.99 Bn |
| Dec 31, 2024 | 3.88 Bn |
| Sep 30, 2024 | 3.70 Bn |
| Jun 30, 2024 | 3.77 Bn |
| Mar 31, 2024 | 3.77 Bn |
| Dec 31, 2023 | 3.59 Bn |
| Sep 30, 2023 | 3.49 Bn |
| Jun 30, 2023 | 3.55 Bn |
| Mar 31, 2023 | 3.60 Bn |
| Dec 31, 2022 | 3.36 Bn |
| Sep 30, 2022 | 3.25 Bn |
| Jun 30, 2022 | 3.33 Bn |
| Mar 31, 2022 | 3.35 Bn |
| Dec 31, 2021 | 3.14 Bn |
| Sep 30, 2021 | 2.96 Bn |
API Access
Automatic Data Processing 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=ADP&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "operating-expenses", "ticker": "ADP", "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=ADP&period=max&api_key=YOUR_API_KEY");
const data = await res.json();