C3.ai (AI) Change in Accured Expenses (2020 - 2026)
C3.ai (AI) reported Change in Accured Expenses of -$2.05 million for fiscal Q1 2027 (quarter ended Jul 31, 2026), compared with $3.34 million a year earlier.
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Analysis
C3.ai (AI) Change in Accured Expenses (2020 - 2026) Analysis & Trends
Over the twelve months ended Jul 31, 2026, C3.ai's Change in Accured Expenses came in at -$9.84 million; for FY2026 (ended Apr 30, 2026), it came in at -$4.44 million.
- By fiscal year, Change in Accured Expenses came in at $8.71 million in FY2025, -$6.22 million in FY2024, $3.19 million in FY2023 (-69.3%) and $10.39 million in FY2022 (+27.8%).
- Five-year quarterly Change in Accured Expenses spans a low of -$9.32 million in fiscal Q4 2026 and a high of $12.46 million in fiscal Q3 2025.
- The high point for year-over-year Change in Accured Expenses in five years was fiscal Q3 2025 (growth of 357.9%); the low point was fiscal Q4 2023 (a decline of 78.0%).
- Per Business Quant data, the three fiscal quarters before Q1 2027 came in at -$9.32 million (Q4 2026), $3.81 million (Q3 2026) and -$2.27 million (Q2 2026).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | Change Accured Exp. (Qtr) |
|---|---|---|---|---|---|
| 1 | Palo Alto Networks | 328.64 Bn | 313.71 Bn | 2.30 Bn | 369.00 Mn |
| 2 | CrowdStrike Holdings | 276.50 Bn | 256.94 Bn | 1.10 Bn | 72.38 Mn |
| 3 | Fortinet | 132.74 Bn | 118.67 Bn | 1.64 Bn | 75.10 Mn |
| 4 | Snowflake | 120.19 Bn | 107.50 Bn | 1.04 Bn | 108.85 Mn |
| 5 | Datadog | 99.51 Bn | 81.15 Bn | 881.34 Mn | 6.98 Mn |
| 6 | Okta | 35.35 Bn | 25.44 Bn | 641.00 Mn | -2.00 Mn |
| 7 | Axon Enterprise | 33.58 Bn | 28.10 Bn | 546.45 Mn | 256.58 Mn |
| 8 | Zscaler | 32.06 Bn | 18.18 Bn | - | - |
| 9 | MongoDB | 28.84 Bn | 19.30 Bn | 569.77 Mn | 43.09 Mn |
| 10 | C3.ai | 1.71 Bn | -815.39 Mn | 16.67 Mn | -2.05 Mn |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jul 31, 2026 | -2.05 Mn |
| Apr 30, 2026 | -9.32 Mn |
| Jan 31, 2026 | 3.81 Mn |
| Oct 31, 2025 | -2.27 Mn |
| Jul 31, 2025 | 3.34 Mn |
| Apr 30, 2025 | 1.06 Mn |
| Jan 31, 2025 | 12.46 Mn |
| Oct 31, 2024 | -3.06 Mn |
| Jul 31, 2024 | -1.76 Mn |
| Apr 30, 2024 | -6.39 Mn |
| Jan 31, 2024 | 2.72 Mn |
| Oct 31, 2023 | -2.51 Mn |
| Jul 31, 2023 | -39,000.00 |
| Apr 30, 2023 | 2.12 Mn |
| Jan 31, 2023 | 2.12 Mn |
| Oct 31, 2022 | -1.54 Mn |
| Jul 31, 2022 | 491,000.00 |
| Apr 30, 2022 | 9.64 Mn |
| Jan 31, 2022 | 6.11 Mn |
| Oct 31, 2021 | 1.72 Mn |
API Access
C3.ai Change in Accured 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=change-in-accured-expenses&ticker=AI&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "change-in-accured-expenses", "ticker": "AI", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=change-in-accured-expenses&ticker=AI&period=max&api_key=YOUR_API_KEY");
const data = await res.json();