AI bank reconciliation in 10 minutes | EP 113

Bank reconciliation is the task everyone wants to automate, and before today's AI tech, nobody could. Alane Boyd and Micah Johnson explain why it stayed manual for so long, then walk through the exact prompt that turns eight to ten hours of line-item matching into a short 10 min review.

When you export your statements from your bank and QuickBooks (or another accounting platform), there are no matching IDs, no shared descriptions, and nothing a traditional automation could key off; somebody had to sit down and compare them line by line, and that somebody was expensive.

In this episode, you’ll learn:

• Why rules-based automation could never reconcile your books and what changed when AI could read AWS, Amazon Web Services, and Amazon Web SVCS as one vendor
• The three-rule starting prompt that matches on description, amount, and a three-day date window
• How to read the exceptions when AI explains that a check has not cleared or a bank fee has not hit QuickBooks yet
• How to turn the first run into a reusable skill so you never re-explain your setup again
• Where the human stays in the loop, because the matching is low-value work and the exceptions are not
• How the same approach applies to investment property statements, media buys, and anything else with two lists that should agree

Reconciliation might be a boring task, but that's exactly why it is a great place to point AI first. Press play and start automating your reconciliation workflow this week.

This episode is brought to you by Biggest Goal, the company Alane Boyd and Micah Johnson co-founded.

Fifteen years of scaling their own companies taught them that adoption is a people problem long before it's a technology problem, so we train executives and their teams through workshops, cohorts, mastermind groups, and hands-on support.

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