AI for AR Reporting: Where to Start

About this webinar

Join LedgerUp on Zoom on Thursday, November 19, 2026, from 1:00 to 1:45 p.m. PST for an educational session on getting started with AI-powered accounts receivable reporting. Bailey Spell will open the session with an introduction to the reporting challenges finance teams can tackle first. Q&A follows the five-part educational agenda.

The focus is practical education, not a product pitch. We’ll explain the reporting fundamentals, walk through a focused starting approach, and show how to evaluate where AI helps, and where reliable calculations and human judgment still matter. No prior AI expertise is required.

If your AR reporting still means exporting spreadsheets, reconciling invoice and payment data, and writing the same commentary every week, this session will help you decide what to automate first, and what should stay under human review.

We’ll start with a focused pilot: a weekly AR aging and collections-priority report. Learn how to define the questions it should answer, align invoice and payment data, and use AI to draft explanations of overdue balances, changes in aging, and accounts that need attention.

We’ll cover the controls that make the report useful: consistent metric definitions, links back to source records, checks for missing or stale data, and finance approval before summaries are shared or used to trigger action.

Built for finance, AR, and revenue operations teams looking for an achievable first project, not a replacement for their accounting system. Leave with a clear pilot scope, a data-readiness checklist, and a way to measure whether the automation is saving time without sacrificing accuracy.

What we’ll cover

1. Opening remarks with Bailey Spell

   Set the learning goals, frame the common challenges in AR reporting, and introduce how to choose a manageable first automation project.

2. Choose the first report, not every report

   Start with a weekly aging and collections-priority report. Define its audience, decisions, cadence, and what a useful result looks like.

3. Get the underlying data ready

   Map invoice balances, due dates, customer identifiers, payments, credits, and disputes. Agree on the reporting cutoff and aging definitions before adding AI.

4. Use AI where it adds value

   Draft plain-language variance commentary, flag unusual changes, and summarize follow-up priorities while keeping calculations tied to verified source data.

5. Build in finance review

   Check totals against the accounting system, surface stale or missing inputs, protect customer information, and require review before distribution or downstream action.

6. Turn the pilot into a repeatable workflow

   Define ownership, exception handling, and success measures such as report preparation time, corrections needed, and time from insight to follow-up.

7. Q&A

   Bring your questions about choosing a first AR reporting project, preparing the data, and reviewing AI-generated insights. Q&A follows the educational agenda.

Opening speaker

• Bailey Spell

  Opening speaker