The session replays from the AFP FP&A Series | Building Financial Intelligence: Deliberate Practice in an AI Era are now available for you to view on demand through Wednesday, October 21.
This FP&A Series is focused on the real work of finance: framing questions, testing assumptions, translating business reality into financial logic and communicating what to do next.
Sessions:
- Designing Finance AI That Builds Acumen
- The Human Layer of Owning AI Output
- Applying Financial Intelligence: With AI—and Our Own Judgment
- Why AI Agents Haven't Worked in Finance, and What Finally Changed (Bonus session not eligible for CTP/CCM or CPE credits)
Designing Finance AI That Builds Acumen
AI is changing finance work, but its greatest value comes when professionals design workflows that strengthen both the system and their own judgment. Explore a practical framework for structuring AI-enabled finance work across three layers: automation + computation, synthesis + analysis, and editing + expertise. The session will show how thoughtful design in establishing the process, and feedback through iterative cycles, can improve outputs over time while also accelerating the development of business acumen. Attendees will leave with a clearer view of how to use AI to scale efficiency without giving up the critical thinking that defines strong finance leadership.
Learning Objectives:
- Identify the finance considerations that need to be built into each layer of the framework, including the different types of AI-assistants and the appropriate level of review, validation, and accountability for each.
- Apply a practical verification framework to ensure AI-generated analysis and commentary are accurate, defensible, and ready for stakeholder discussion.
One of the key themes in Carolina Lago’s session, Designing Finance AI That Builds Acumen, is that better decisions come not from more analysis, but from combining data, business context, and human judgment. This case study illustrates that idea in practice. Using the same contribution-margin data and the same analytical workflow, two different conclusions emerged because of one additional piece of business context that never existed in the source data. As you review the case, consider how finance teams can design processes that leverage AI and automation for analysis while still capturing the institutional knowledge, assumptions, and strategic intent that often determine the right business decision.
The Human Layer of Owning AI Output
Setting up the AI processes is half the story; the second act is evaluating the outputs so that you can confidently take responsibility for what gets delivered. Building on the three layers framework in the first webinar help finance professionals understand what it means to own AI-assisted work. Attendees will explore where risk shows up, how verification practices should change based on the type of work, and what it takes to confidently defend AI-supported insights with stakeholders. The session offers a practical framework for strengthening accountability, credibility, and trust in an AI-enabled finance environment, because AI is not presenting to the CFO, you are.
Learning Objectives:
- Learn the four questions that catch the most common AI errors in financial analysis and commentary, and how to run them in under ten minutes before anything leaves your desk.
- Close the gap between "AI produced this" and "I own this," including develop the evidence trail that makes your AI-assisted work defensible to leadership, to auditors, and to yourself.
Applying Financial Intelligence: With AI—and Our Own Judgment
Financial intelligences is not just about the numbers—they’re about strategy, risk, and context. So, can AI actually handle all of that? In this session, we walk through a buy-versus-lease to show how AI can model scenarios, pressure-test assumptions, and quantify the impact across key financial statements and metrics. We highlight where AI speeds up the work—and where finance still needs to step in to interpret results, challenge the story, and bring broader business judgment. We’ll close with the final decision and rationale, and what it means for how FP&A can partner with AI while sharpening its own decision-making edge.
Learning Objectives:
- Identify the critical questions that underpin sound financial analysis—whether performed by AI or in spreadsheets.
- Distinguish where AI accelerates modeling and where human judgment, context, and business expertise drive better decisions.
Click below to watch Ramp's session, Why AI Agents Haven't Worked in Finance, and What Finally Changed. This session is not eligible for CTP/CCM or CPE credits.