12workpapers in this file
AI products for finance teams, read against the job they claim to do
93 vendor records and 19 filing excerpts, set out one finance task at a time.
Each note takes one finance task, sets out what the work involves and where a person must stay in the loop, then lists the products whose makers say they address it. What a maker says its AI does, the data it names, where a person reviews and how data is handled are quoted word for word, in quotation marks, from the maker's own pages or SEC filings, with the address and the date read. The short summary of each product and the lists of use cases and named data are ours, written from the same pages and labelled as ours.
Lead schedule.The twelve workpapers by the data their makers name
One stroke per record, in fives. Systems named by 4 or more records; every system is in Appendix C. Footed: a record filed under two workpapers counts in both.
The schedule scrolls sideways; the workpaper column stays in place.
| W/P | Banks | NetSuite | ERP | SAP | Cards | Intacct | Workday | Claude | CRM | QuickBooks | Dynamics | Oracle | Payments | Payroll | Salesforce | No data named |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1Forecasting | 4 | 2 | 5 | 1 | 1 | 1 | 1 | 3 | 3 | 1 | 1 | 1 | 1 | 9 | ||
| 2Accounts payable | 2 | 1 | 2 | 2 | 1 | 2 | 1 | 12 | ||||||||
| 3Expense audit | 1 | 1 | 2 | 1 | 2 | 1 | 1 | 2 | ||||||||
| 4Collections and cash application | 3 | 5 | 2 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 8 | |||
| 5Reconciliation | 7 | 2 | 1 | 1 | 3 | 1 | 1 | 1 | 2 | 3 | 7 | |||||
| 6Close automation | 7 | 1 | 1 | 1 | 4 | 1 | 1 | 1 | 1 | 2 | 4 | 1 | 7 | |||
| 7Variance analysis | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 10 | ||||||
| 8Contract and lease data | 1 | 1 | 3 | 1 | 1 | 2 | 2 | |||||||||
| 9Tax research | 4 | |||||||||||||||
| 10Treasury and FX | 4 | 2 | 2 | 2 | 2 | 2 | 1 | |||||||||
| 11Payment anomalies and fraud | 2 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 6 | ||||
| 12Audit preparation | 1 | 4 | ||||||||||||||
| Footed | 27 | 18 | 17 | 14 | 11 | 10 | 10 | 8 | 8 | 6 | 7 | 6 | 7 | 8 | 5 | 72 |
Answer sheet.What the makers' pages answer, workpaper by workpaper
One stroke per record: solid where the maker's own page answers the question, dotted where the pages read are silent. A count of what the pages say, not a score.
| W/P | Data it names | Where a person reviews | Model or data handling |
|---|---|---|---|
| 1Forecasting | 12 of 21 | 4 of 21 | 15 of 21 |
| 2Accounts payable | 7 of 19 | 10 of 19 | 10 of 19 |
| 3Expense audit | 7 of 9 | 4 of 9 | 2 of 9 |
| 4Collections and cash application | 9 of 17 | 7 of 17 | 6 of 17 |
| 5Reconciliation | 8 of 15 | 10 of 15 | 2 of 15 |
| 6Close automation | 10 of 17 | 15 of 17 | 3 of 17 |
| 7Variance analysis | 7 of 17 | 8 of 17 | 11 of 17 |
| 8Contract and lease data | 5 of 7 | 3 of 7 | 2 of 7 |
| 9Tax research | 1 of 5 | 2 of 5 | 2 of 5 |
| 10Treasury and FX | 5 of 6 | 2 of 6 | 5 of 6 |
| 11Payment anomalies and fraud | 6 of 12 | 5 of 12 | 5 of 12 |
| 12Audit preparation | 3 of 7 | 6 of 7 | 2 of 7 |
Contents and list of exhibits
The twelve workpapers in the order of the finance calendar (plan, record, close, report, assure), each with its products exhibit, numbered as in the note.
| W/P | Use case | The question it answers | Products exhibit | Products |
|---|---|---|---|---|
| 1 | Cash-flow and revenue forecasting | Which AI products produce or adjust revenue and cash forecasts, from what data, and who checks the result? | Exhibit 1.3 | 21 |
| 2 | Accounts payable invoice capture and coding | What do AI products in accounts payable extract, code and approve, and where must a person still sign? | Exhibit 2.4 | 19 |
| 3 | Expense audit | How do AI products audit employee expenses, and what is left for a person to decide? | Exhibit 3.4 | 9 |
| 4 | Collections and cash application | How do AI products match payments to invoices and decide who to chase, and what stays with the credit team? | Exhibit 4.3 | 17 |
| 5 | Account reconciliation | How do AI products match transactions and prepare reconciliations, and how do exceptions reach a reviewer? | Exhibit 5.4 | 15 |
| 6 | Month-end close automation | Which steps of the month-end close do AI products take on, and which stay with the controller? | Exhibit 6.4 | 17 |
| 7 | Variance analysis and management commentary | Can AI products explain why the numbers moved, and who stands behind the commentary they draft? | Exhibit 7.3 | 17 |
| 8 | Contract and lease data extraction | Can AI products pull the terms finance needs out of contracts and leases, and how is the extracted data checked? | Exhibit 8.2 | 7 |
| 9 | Tax research and provision support | Can AI products answer tax questions and support compliance work reliably, and who is responsible for the answer? | Exhibit 9.3 | 5 |
| 10 | Treasury and FX | Where do AI products help treasury with cash and currency risk, and which decisions must stay with the treasurer? | Exhibit 10.2 | 6 |
| 11 | Anomaly and fraud detection in payments | Can AI products catch fraudulent or anomalous payments before money leaves, and what controls must stay with people? | Exhibit 11.3 | 12 |
| 12 | Audit preparation and evidence | How do AI products prepare audit evidence and documentation, and what must a person review before it is relied on? | Exhibit 12.4 | 7 |
Source: vendor records on this site. A product count is the vendor records whose quoted claims address the use case; a vendor appears under every use case its pages address.
Appendices
Computed from the records at each build: every product or filing set side by side on one question.
- CSystems and data sources the makers name30 systems
- DWhere a person reviews, in the makers' words45 statements
- EWhat SEC filers say about AI in their own finance work19 excerpts