From AI Experimentation to Implementation: A Finance Leader's Starting Point

Finance leaders don't lack exposure to AI. They lack a framework for turning that exposure into implementation. The teams making real progress share a few habits: they start with a concrete process, they define what 'good' looks like before they touch a tool, and they keep a human in the loop on anything that touches reported numbers.

A useful first step is to inventory the repetitive, judgment-light work in your week: reconciliation tie-outs, commentary drafting, variance explanations, data cleanup. These are the places AI can create time without creating risk. Pick one, define the inputs and the expected output, and run it alongside your existing process.

Implementation is less about the tool and more about the workflow around it. Who reviews the output? Where does the source data live? What happens when the AI is wrong? Answering those questions is the work, and it's work finance leaders are uniquely qualified to lead.