Automatic Data Processing (ADP) Enterprise Value (2009 - 2026)
Automatic Data Processing (ADP) posted Enterprise Value of $102.66 billion for fiscal Q1 2027 (quarter ended Sep 30, 2026), down 7.4% from $110.89 billion a year earlier but up 21.0% from the prior quarter.
Automatic Data Processing (ADP) Enterprise Value (2009 - 2026) Analysis & Trends
For FY2026 (ended Jun 30, 2026), Automatic Data Processing's Enterprise Value came in at $84.86 billion, down 27.6% from FY2025.
- Annual Enterprise Value shows a five-year compound annual growth rate of 0.8% (FY2021 to FY2026).
- In prior fiscal years, Automatic Data Processing's Enterprise Value was $117.15 billion in FY2025 (+24.5%), $94.11 billion in FY2024 (+6.4%), $88.49 billion in FY2023 (+2.9%) and $85.96 billion in FY2022 (+5.4%).
- Quarterly Enterprise Value has run from a low of $78.17 billion in fiscal Q3 2026 to a high of $121.46 billion in fiscal Q3 2025 over five years.
- On a year-over-year basis, Enterprise Value has declined in each of the last four quarters, with an average decline of 0.9% over the last eight quarters.
- The strongest year-over-year quarter for Enterprise Value in the past five years was fiscal Q2 2022, with growth of 38.0%; the weakest was fiscal Q3 2026, with a decline of 35.6%.
- According to Business Quant data, Enterprise Value for the three prior fiscal quarters was $84.86 billion (Q4 2026), $78.17 billion (Q3 2026) and $101.19 billion (Q2 2026).
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) |
|---|---|---|---|---|
| 1 | Palantir Technologies | 413.65 Bn | 378.59 Bn | 1.93 Bn |
| 2 | Oracle | 374.28 Bn | 246.84 Bn | - |
| 3 | Sap Se | 294.90 Bn | 222.88 Bn | 8.64 Bn |
| 4 | Salesforce | 186.99 Bn | 142.87 Bn | 8.70 Bn |
| 5 | Automatic Data Processing | 120.08 Bn | 106.87 Bn | 2.53 Bn |
| 6 | ServiceNow | 113.02 Bn | 92.28 Bn | 2.90 Bn |
| 7 | Intuit | 95.72 Bn | 75.07 Bn | 3.44 Bn |
| 8 | Relx | 74.73 Bn | 73.84 Bn | - |
| 9 | Strategy | 57.35 Bn | 44.49 Bn | 89.63 Mn |
| 10 | Thomson Reuters | 52.16 Bn | 48.13 Bn | - |
Historic Data
| Date | Value |
|---|---|
| Sep 30, 2026 | 99.38 Bn |
| Jun 30, 2026 | 84.86 Bn |
| Mar 31, 2026 | 78.17 Bn |
| Dec 31, 2025 | 101.19 Bn |
| Sep 30, 2025 | 110.89 Bn |
| Jun 30, 2025 | 117.15 Bn |
| Mar 31, 2025 | 121.46 Bn |
| Dec 31, 2024 | 116.90 Bn |
| Sep 30, 2024 | 105.48 Bn |
| Jun 30, 2024 | 94.11 Bn |
| Mar 31, 2024 | 99.05 Bn |
| Dec 31, 2023 | 94.04 Bn |
| Sep 30, 2023 | 97.61 Bn |
| Jun 30, 2023 | 88.49 Bn |
| Mar 31, 2023 | 90.23 Bn |
| Dec 31, 2022 | 97.64 Bn |
| Sep 30, 2022 | 92.71 Bn |
| Jun 30, 2022 | 85.96 Bn |
| Mar 31, 2022 | 93.52 Bn |
| Dec 31, 2021 | 101.92 Bn |
Automatic Data Processing Enterprise Value 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=enterprise-value&ticker=ADP&period=max&api_key=YOUR_API_KEY
import requests
url = "https://data.businessquant.com/historic"
params = {"slug": "enterprise-value", "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=enterprise-value&ticker=ADP&period=max&api_key=YOUR_API_KEY");
const data = await res.json();