AI FAQs for Accountants 

Date: 2/4/25
Time: 12:00 PM - 2:00 PM EST
CPE Credit: 2 hours

The "AI FAQs for Accountants" course is designed to build a foundational understanding of generative AI concepts tailored specifically for accounting professionals. Through a series of frequently asked questions, participants will deepen their knowledge of AI terminology, processes, and applications relevant to the accounting field. The course covers topics from generative AI basics and how ChatGPT works, to practical insights into automation bias, the ethical implications of AI in decision-making, and its transformative impact on client engagement and personalized financial services. This interactive course challenges learners with real-world scenarios, encouraging them to apply their understanding of AI in accounting contexts, thereby fostering confidence and proficiency in using AI responsibly and effectively.

Program Content

1. Understanding Generative AI: Introduction to generative AI, its distinction from traditional AI, and examples of its applications in accounting.
2. Large Language Models (LLMs): Insights into LLMs, such as ChatGPT, and how they support data analysis, report generation, and client communication.
3. How Generative AI Learns: Overview of deep learning and vector-based text processing in AI.
4. Ethics and Automation Bias: Ethical considerations in AI usage, particularly automation bias and the risks of over-relying on AI outputs.
5. AI Capabilities in Business: Examination of ChatGPT’s applications in data analysis, meeting summaries, and client-facing tasks.
6. Preparing Accountants for AI: Practical advice for AI integration, fostering AI-ready leadership, and supporting clients with AI insights.

1. Distinguish between generative AI and traditional AI, recognizing generative AI's ability to create content based on learned patterns.
2. Explain the function and potential of Large Language Models in automating and enhancing accounting processes.
3. Describe ChatGPT's text processing and predictive capabilities, including the roles of deep learning and word vectors.
4. Identify risks like automation bias and understand ethical considerations in AI-driven decision-making.
5. Assess AI's role in enhancing client service, from generating reports to answering client questions, in a reliable and responsible manner.
6. Apply practical insights on incorporating AI into workflows, fostering a culture of continuous learning and collaboration in their teams.