Automatic Data Processing (ADP) Accumulated Expenses (2009 - 2026)
Automatic Data Processing's (ADP) quarterly Accumulated Expenses came in at $3.2 billion in Q2 2026, up 4.08% year-over-year from $3.1 billion in Q2 2025, and up 6.82% quarter-over-quarter from $3.0 billion in Q1 2026.
Automatic Data Processing (ADP) Accumulated Expenses (2009 - 2026) Analysis & Trends
Automatic Data Processing (ADP) has reported Accumulated Expenses for 18 consecutive years, with $3.2 billion the latest figure, recorded in Q2 2026.
- On a quarterly basis, Accumulated Expenses rose 4.08% year-over-year to $3.2 billion in Q2 2026; TTM through Jun 2026 was $3.2 billion, a 4.08% increase from a year earlier, with the FY2026 full-year figure at $3.2 billion, up 4.08% from the prior year.
- Accumulated Expenses was $3.2 billion for Q2 2026 at Automatic Data Processing, up from $3.0 billion in the prior quarter.
- Over five years, Accumulated Expenses peaked at $3.7 billion in Q1 2024 and troughed at $2.1 billion in Q1 2022.
- A 5-year average of $2.7 billion and a median of $2.8 billion in 2023 frame the typical range for Accumulated Expenses.
- Across the five-year window, Accumulated Expenses jumped 69.72% in 2024 and declined 19.85% in 2025, its largest moves.
- Over 5 years, Accumulated Expenses stood at $2.4 billion in 2022, then climbed by 11.02% to $2.6 billion in 2023, then advanced by 17.22% to $3.1 billion in 2024, then fell by 19.77% to $2.5 billion in 2025, then advanced by 29.88% to $3.2 billion in 2026.
- The last three Accumulated Expenses figures came in at $3.2 billion (Q2 2026), $3.0 billion (Q1 2026), and $2.5 billion (Q4 2025), per Business Quant data.
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palantir Technologies | 460.85 Bn | 451.55 Bn | 1.64 Bn |
| 2 | Oracle | 437.09 Bn | 400.44 Bn | - |
| 3 | Sap Se | 258.74 Bn | 237.66 Bn | 8.40 Bn |
| 4 | Salesforce | 195.52 Bn | 184.12 Bn | 8.70 Bn |
| 5 | ServiceNow | 145.61 Bn | 140.94 Bn | 2.82 Bn |
| 6 | Automatic Data Processing | 104.89 Bn | 100.66 Bn | 2.51 Bn |
| 7 | Intuit | 76.97 Bn | 69.77 Bn | 3.46 Bn |
| 8 | Relx | 60.41 Bn | 60.04 Bn | - |
| 9 | Strategy | 57.09 Bn | 54.64 Bn | 81.55 Mn |
| 10 | Workday | 46.40 Bn | 42.99 Bn | 2.21 Bn |
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | 3.22 Bn |
| Mar 31, 2026 | 3.01 Bn |
| Dec 31, 2025 | 2.48 Bn |
| Sep 30, 2025 | 2.91 Bn |
| Jun 30, 2025 | 3.09 Bn |
| Mar 31, 2025 | 2.98 Bn |
| Dec 31, 2024 | 3.09 Bn |
| Sep 30, 2024 | 2.97 Bn |
| Jun 30, 2024 | 3.35 Bn |
| Mar 31, 2024 | 3.71 Bn |
| Dec 31, 2023 | 2.63 Bn |
| Sep 30, 2023 | 2.29 Bn |
| Jun 30, 2023 | 2.34 Bn |
| Mar 31, 2023 | 2.19 Bn |
| Dec 31, 2022 | 2.37 Bn |
| Sep 30, 2022 | 2.17 Bn |
| Jun 30, 2022 | 2.11 Bn |
| Mar 31, 2022 | 2.10 Bn |
| Dec 31, 2021 | 1.94 Bn |
| Sep 30, 2021 | 1.80 Bn |
Automatic Data Processing Accumulated 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=accumulated-expenses&ticker=ADP&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "accumulated-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=accumulated-expenses&ticker=ADP&period=max&api_key=YOUR_API_KEY");
const data = await res.json();