Human-in-the-loop oversight is not optional in a finance context. Every AI output that influences a decision needs to be auditable: where did the data come from, how was the model constructed, what assumptions were applied, and who approved the output. Without this chain, outputs cannot be challenged, improved, or defended. They also cannot be trusted by the people asked to act on them.
The trust problem is practical, not philosophical. If your FP&A team cannot explain why the AI forecast is what it is, they will not stand behind it in a leadership meeting. If business partners suspect the numbers are opaque, they will build their own. If the board cannot trace an assumption back to a source, they will ask for the spreadsheet instead. Governance is the mechanism that makes AI outputs organisationally credible, not just technically accurate.
Governance retrofitted after the fact is significantly harder and more expensive than governance built in from the start. Decisions made early about explainability standards, approval workflows, model documentation, and exception-handling define whether the function can scale AI responsibly. Decisions left unmade create technical debt and compliance risk simultaneously.
