AFP FP&A Series: Credit Reporting

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.

Speaker:

Carolina Lago
Managing Director, Tactic Finance

 

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.

Speaker:

Anna Tiomina
Founder, Blend2Balance

 

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.

Speakers:

Mario Vasquez, FPAC
Sr. Director, Finance, The E.W. Scripps Company

Paul Barnhurst
Founder, The FP&A Guy

Making AI useful for finance requires three things: human-defined rules and guardrails to shape the work, consistent deterministic logic to produce repeatable outputs, and controlled reasoning—through orchestration, tools, and memory—to make agents reliable, transparent, and useful.

In this session, Glenn Hopper will show how to harmonize those forces through the practical example of a 13-week cash flow forecast. The example demonstrates both the insight finance must provide—judgment, assumptions, risk awareness, and decision context—and the strength of a well-built agent harness to deliver the control, transparency, and repeatability finance needs. Attendees will leave with a clearer view of how AI can accelerate finance workflows while preserving the human judgment required to move from inputs to action.

Learning Objectives:

• Define an “agent harness” and how it makes probabilistic AI agents reliable enough for finance use.
• Evaluate a working agent-driven 13-week cash flow forecast, including where deterministic controls, probabilistic methods, and human-review gates each belong in the workflow.

Speaker:

Glenn Hopper
Managing Director, Head of AI, VAi Consulting