AI in the Office of the CFO: From Reporting to Real Time Decisions

Organizations expect finance to move faster and provide more decision-ready signals. Yet many CFOs remain cautious: can AI copilots be trusted during the close, will forecasts improve, and how do we prove value to the board? This session addresses the practical shift from retrospective reporting to real time guidance. We will start by examining where FP&A copilots have actually been applied inside proper close cycles, what success looked like, and which workflows changed as a result. That discussion will ground the rest of the conversation in tangible examples rather than abstract promises. The middle of the session focuses on the foundations that make AI outputs reliable. Participants will explore the data and governance requirements needed before deploying copilots, including data lineage, consistent master data, and clear ownership for inputs and outputs. We will also unpack model risk and explainability, and what an audit trail for AI-driven decisions should include so that finance can remain compliant and accountable. Practical measurement will be a central theme, with a move away from headcount-only metrics toward measures such as time to close, forecast accuracy, decision cycle time, and the business outcomes influenced by faster guidance. Finally, the discussion turns to people and governance. We will consider how to reskill finance teams for an AI-enabled operating model, what responsibility the CFO should take in prioritizing and funding AI investments, and how to align stakeholders across finance, risk, and technology.

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