Applied AI for Accounting Workflows

What you'll learn

• Describe how large language models generate output, including tokens, context windows, temperature, and the hallucination failure mode, and identify tasks for which they are and are not appropriate.
• Identify the data-privacy and confidentiality safeguards required before client information may be used with an AI tool, including the distinction between consumer and enterprise tiers, training and retention settings, and de-identification.
• Apply the professional-responsibility principle of human-in-the-loop review, in which a qualified professional reviews and remains responsible for every AI-assisted output.
• Apply a structured prompting method using role, context, task, and format, strengthened with examples, grounding, reasoning requests, and guardrail phrasing.
• Evaluate the output of at least ten accounting AI workflows against their source material and select an appropriate model tier for each task.
• Apply the cost-segregation intake triage as a preliminary, professionally reviewed step that does not replace a full engineering-based study, and apply citation-verification discipline to research output.
• Evaluate AI tools and build a firm AI-use policy, cost-management approach, and adoption path that keep confidentiality and review controls in place.

Syllabus

6 modules, about 100 minutes of reading, free to read in full without an account. QAS Self Study; program level Basic to Intermediate; prerequisites: None; advance preparation: None.

1. 1. Foundations: What Large Language Models Are and How to Think About Them in a Firm 18 min
2. 2. Data Privacy, Confidentiality, and Professional Responsibility 16 min
3. 3. Prompting Technique for Accountants 17 min
4. 4. The Workflow Prompt Library, Part One 16 min
5. 5. The Workflow Prompt Library, Part Two 16 min
6. 6. Building an AI-Enabled Firm Workflow, Safely and Cheaply 17 min