This course introduces accounting, auditing, and finance professionals to the emerging field of agentic AI: autonomous, goal-driven systems capable of planning, acting, and learning with minimal human intervention. Participants will explore how agentic AI differs from traditional automation (RPA) and generative AI, and how these agents are being deployed in finance and accounting. The course emphasizes foundational concepts such as the Perceive–Reason–Act–Learn (PRAL) loop and ethical decision-making frameworks. To ground abstract ideas in practice, participants will experience a brief agent simulation using basic ChatGPT, showcasing how natural language prompts can emulate agentic behavior, such as goal setting, clarification loops, and structured task execution. The course also highlights real-world agent platforms (e.g., Microsoft Copilot, IBM watsonx, and UiPath) to provide context on enterprise adoption trends and discusses emerging global regulations such as the EU AI Act and the U.S. Executive Order on Safe AI.
Learning Objectives
- Define agentic AI and differentiate it from generative AI and RPA
- Identify core use cases of agentic AI in finance and accounting, including scenario modeling and financial close automation
- Recognize the potential benefits and risks of deploying autonomous agents in financial workflows
- Understand global regulatory frameworks that govern agentic AI
- Assess the role of the accounting professional in ensuring responsible AI adoption
Major Topics
- Evolution from RPA to agentic AI (PRAL cycle: perceive, reason, act, and learn)
- Use cases in financial reporting and decision support
- Platform overview: simulating agents with ChatGPT; enterprise examples from IBM watsonx, Microsoft Copilot, and UiPath
- Global regulation: EU AI Act, U.S. Executive Order, AICPA Code of Conduct
- Ethical considerations and professional skepticism in autonomous systems
