Covered in This Course
- Design governance controls for AI-assisted finance workflows, including source-grounding, reproducibility, version control, audit trails, and disclosure practices.
- Define the appropriate role of human oversight by establishing review checkpoints, exception handling processes, accountability structures, and approaches for maintaining stakeholder trust as AI systems evolve.
Description
As AI gets embedded in finance workflows, governance is what lets you scale it. Without controls, you can only automate as much as one person can check by hand.
In this hands-on course, you’ll learn how to create the controls and human oversight needed to use AI responsibly and reliably at scale. Building on the orchestrated workflow developed in Orchestrating Agentic Workflows, you’ll explore how changes in AI models, source data and business context can affect results and what finance teams can do to manage those changes.
You’ll apply four practical governance controls to an AI-enabled finance process: source-grounding, reproducibility, version control and disclosure. You’ll also identify where human judgment remains essential, including designing workflows, reviewing outputs, managing exceptions and maintaining accountability for final decisions.
Finally, you’ll put these concepts into practice by creating a concise briefing that explains how an AI-assisted workflow operates and why stakeholders can trust its outputs.
Agent Governance and the Human Role is the third course in the series, bringing together AI design, orchestration and governance to help you build finance workflows that can scale with confidence. This course prepares attendees to participate in the AFP Hackathon at the 2027 AFP FP&A Forum in New Orleans this March.
This educational badge series uses the following definition of an agent: an AI model equipped with tools and guided by instructions and guardrails that allow it to perform a defined job or workflow. This aligns with definitions from frontier models, although there is a bit of wiggle room for differences.
The labs start deliberately with human-guided agents rather than full autonomy. Keeping a person in the loop makes it easier to learn how an agent works, see where it succeeds or fails, and practice the judgment and governance needed to use it responsibly. Think of this as the foundation, not the finish line. At the FP&A Forum, we will build on that foundation and explore what it takes to let agents run more autonomously, and when finance should and shouldn't let them.