Finance automation should improve preparation, comparison, exception detection, and narrative support without allowing a model to become the accounting authority. Every lesson separates assistance from approval.
What You Will Be Able to Do
- Identify suitable and unsuitable accounting uses.
- Design source-linked extraction and exception workflows.
- Preserve materiality, approval boundaries, and audit evidence.
- Separate forecasting data and calculation from assumptions and accountable judgment.
Guided Sequence
Boundaries and structured preparation
- Where AI Helps and Fails in Accounting — An accounting use-case matrix separating suitable assistance from prohibited or controlled decisions.
- Expense Categorization with LLMs — An expense taxonomy with evidence fields, confidence thresholds, exceptions, and approval controls.
- Smart Invoicing with AI — An invoice exception matrix linking extraction confidence, control failures, reviewers, and payment boundaries.
- AI for Financial Document Review — A document-review protocol defining extraction fields, evidence links, exceptions, and sign-off.
Close and assurance
- AI for Reconciliations and Month-End Close Support — A close-support design with break categories, evidence requirements, reviewer ownership, and sign-off.
- AI for Audit Requests, PBC, and Workpaper Support — An audit-support workflow preserving source lineage, preparer review, approval, and evidence retention.
Planning and cash operations
- Forecast Budgets with AI — A forecasting protocol separating source data, assumptions, scenarios, model output, and accountable judgment.
- AI for Accounts Receivable, Collections, and Cash Application — A receivables workflow map with matching rules, dispute routing, customer controls, and approval boundaries.
Lesson Library
| Lesson | Level | Time | Learner output |
|---|---|---|---|
| Where AI Helps and Fails in Accounting | Beginner | 15 min | An accounting use-case matrix separating suitable assistance from prohibited or controlled decisions. |
| Expense Categorization with LLMs | Intermediate | 15 min | An expense taxonomy with evidence fields, confidence thresholds, exceptions, and approval controls. |
| Smart Invoicing with AI | Intermediate | 15 min | An invoice exception matrix linking extraction confidence, control failures, reviewers, and payment boundaries. |
| AI for Financial Document Review | Intermediate | 15 min | A document-review protocol defining extraction fields, evidence links, exceptions, and sign-off. |
| AI for Reconciliations and Month-End Close Support | Advanced | 20 min | A close-support design with break categories, evidence requirements, reviewer ownership, and sign-off. |
| AI for Audit Requests, PBC, and Workpaper Support | Advanced | 20 min | An audit-support workflow preserving source lineage, preparer review, approval, and evidence retention. |
| Forecast Budgets with AI | Advanced | 20 min | A forecasting protocol separating source data, assumptions, scenarios, model output, and accountable judgment. |
| AI for Accounts Receivable, Collections, and Cash Application | Intermediate | 15 min | A receivables workflow map with matching rules, dispute routing, customer controls, and approval boundaries. |
Completion Standard
A lesson is complete when the stated learner output has been produced, reviewed against representative evidence, and assigned an owner or next decision. Reading the page without producing the artifact is orientation, not completion.
Practice Case
Use the Harborline Services running case when you do not have safely redacted examples from your own organization.
Where to Go Next
- Return to the Academy home
- Browse the Academy Practice Cases