Automate the Boring Match First
When people think about AI in bookkeeping, they often think of the hard cases. A cryptic charge. A one-time payment. A receipt with no obvious vendor.
Most of a month's transactions are not hard. They are the same vendors and amounts, month after month. Let the rules your software already has handle that repeat work before you add AI. What is left is smaller, and it shows where judgment is needed.
Start with rules you already have
Most accounting software can categorize bank transactions by rule: a charge with a certain description or amount goes to a certain account. Depending on your software and settings, a rule fills in the category for you to confirm or posts it on its own. Many programs also match uploaded receipts to bank transactions.
Take a business with a monthly software subscription and a fuel card. The subscription is the easy case: same vendor, same amount, every month. One rule covers it.
The fuel card is harder. Charges come from many stations under many names. A rule that sends anything containing "gas" to fuel expense can also catch the gas utility bill. If the card is only for fuel, tie the rule to the card's account.
Start with rules that suggest, and let one post on its own only after months of being right.
Review the exceptions
Each month, count two things: transactions a rule got wrong, and transactions no rule touched.
Divide the wrong ones by everything the rules handled. That is your error rate. If it is higher than you would accept from a person, fix or narrow the rules before adding anything new.
The untouched ones are your remainder. Track both for a few months to see whether the rules are improving and how much work is left.
Then use AI for the remainder
What remains is usually the messy part: a vague description, a one-time purchase, a new vendor. In an approved tool, AI can suggest a category from the description and the context you give it, or draft a question for the person who made the purchase.
A person should review each suggestion before it posts. A wrong category that looks plausible is harder to catch than a blank one.
The order matters. Rules take the repeat work, a monthly review keeps them accurate, and AI helps with the rest while a person signs off. Each step leaves the next one smaller.