Finance teams face a tension: delivering more insight under constrained resources, compliance demands, and expectations for transparency. AI can help—but the question is less what it can do than whether finance leaders can trust how it operates and how decisions are governed. Stuart Langer offers a finance-first framework for AI across close, reporting, variance analysis, spend tracking, and compliance monitoring. The session examines what separates effective implementations—AI embedded in systems of record, explainable outputs, human-in-the-loop control, and auditable governance—and how external generative AI tools fit alongside accounting systems. Attendees leave with an approach that strengthens financial integrity and stakeholder trust.

Learning Objectives:

• Identify finance workflows where AI can improve efficiency, visibility, and control 

• Explain the importance of confidence, control, and accountability in finance AI adoption 

• Assess how trusted AI can support transparency, internal controls, and better decision-making 

• Apply a practical framework for evaluating organizational readiness and next steps for AI in finance 

• Differentiate among core AI technologies—machine learning, large language models, and agents—and explain where each applies in finance workflows