How to implement AI in finance: Where to start and what to avoid

Finance teams can gain practical value from AI by beginning with one or two measurable, high-volume workflows such as expense processing, payment retries, fraud detection, or reconciliation, rather than attempting broad automation at once. Successful implementation depends heavily on connected financial data across banking, payments, cards, and accounting systems, as fragmented infrastructure can create costly integration work and limit AI’s context. The recommended approach is to establish clear goals and baseline metrics, define approval policies and escalation rules, pilot AI with human oversight, review outcomes regularly, and extend it gradually into related workflows. Performance should be assessed through a small set of relevant quantitative measures, including processing times, acceptance rates, false positives, match rates, and close times, alongside staff feedback and reduced reliance on manual workarounds. The material argues that unified financial platforms, including Airwallex’s offering, can simplify data access and support more scalable AI deployment, while cautioning that governance, auditability, project ownership, and realistic expectations remain important for use in regulated finance operations.

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