Finance transformation rarely stalls because the team cannot produce another spreadsheet. It stalls because work, ownership and controls are spread across too many systems.
Consider a finance operations use case: incoming requests about cost centres, supplier changes, journal support and reporting access. An AI assistant could classify the request, retrieve the relevant policy, identify the system of record and route it to the right owner. That sounds simple. In practice, the value depends on whether the underlying process is clear enough to automate safely.
Before adding AI, I would ask four questions:
1. Who owns each decision, and which decisions require a human sign-off?
2. Is the source data reliable across the ERP, workflow and reporting layers?
3. Can every recommendation be traced to a policy, transaction or approved master-data record?
4. What happens when the assistant is uncertain or finds conflicting evidence?
The strongest starting point is often a narrow workflow with a measurable baseline: time to triage, number of hand-offs, rework rate and exceptions escalated. Keep approval rights with accountable people. Log the evidence behind each recommendation. Then compare the pilot with the baseline before widening the scope.
That is where finance systems experience matters. AI can speed up routing and information retrieval, but it cannot repair ambiguous ownership, inconsistent master data or a weak control design. Fix those foundations and the technology has a much better chance of improving service without making the audit trail harder to follow.
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