Year-End Bootcamp: A Finance Team’s Guide to Using AI to Improve Close Performance

If your year-end close felt harder than it should have, you're not alone. Many finance teams are still losing time to manual reconciliations, exception follow-up across disconnected systems, and reporting work that gets rebuilt every cycle. This session shows where AI can improve close performance in practice, from manual reconciliation and exception handling to more scalable, audit-ready workflows. We’ll break down the AI spectrum (rule-based automation, RPA, machine learning, generative AI, and agentic AI) and map each approach to real finance tasks so you can focus on the use cases that improve close performance fastest. You’ll also learn where AI can reduce manual effort in exception research and variance analysis, and which controls need to be in place, including confidence scoring, approvals, and audit trails. We’ll also cover where AI can extend beyond reconciliation into broader EPM workflows, including forecasting support and anomaly detection, so your team can spend less time reacting and more time advising. This session is ideal for finance teams looking to use AI to reduce close cycle time, improve audit-ready visibility across workflows, and build a more scalable finance operation this year. What you’ll learn:

• The difference between rule-based automation, generative AI, and agentic AI

• Where AI can cut manual work in matching, exceptions, and variance follow-up

• What an autonomous reconciliation process looks like end to end

• How to use AI without losing control of approvals, audit trails, or accountability

• A practical roadmap for integrating AI into broader EPM workflows