AI can help in almost every finance process, but not equally. A useful map sorts use cases by process and by kind of AI.
| Process | Typical AI uses | Kind |
|---|---|---|
| Record to report (close) | Reconciliation matching, anomaly detection on journals, flux and variance commentary, close checklists | Predict + draft |
| Procure to pay (AP) | Invoice capture and coding, duplicate and fraud detection, three-way match exceptions, supplier queries | Predict + act |
| Order to cash (AR) | Cash application, collections prioritization, dispute summaries, payment-reminder drafting | Predict + draft + act |
| FP&A | Driver-based forecasts, scenario modeling, narrative for management packs, ad-hoc analysis | Predict + draft |
| Treasury | Cash forecasting, liquidity scenarios, bank-fee analysis | Predict |
| Tax | Research summaries, provision support, document review, indirect-tax classification | Draft |
| Audit and controls | Full-population testing, evidence gathering, control documentation | Predict + draft |
| Procurement finance | Spend classification and analytics, contract-term extraction | Predict + draft |
What finance teams actually use first
Gartner's 2024 survey found the most common AI uses in finance were intelligent process automation (44% of finance functions) and anomaly and error detection (39%), ahead of analytics (28%) and operational assistance (27%). In other words, the early wins are in transaction-heavy processes, not in clever chat.
How to choose
Score each candidate on:
- Value: hours, cash, errors or decision quality.
- Effort: data readiness, integration, change.
- Risk: what happens if it is wrong, and who would notice.
Start where volume is high, rules are clear, data is clean and a human already reviews the output. Use the hub's use-case catalog and its priority score (2 × value − effort − risk) to build your first list.