I booked 30 minutes last week to help a client automate a reconciliation. We spent three hours. The tool was not the problem. The problem was that the data would not line up, and no automation runs until you fix that first.
That is the quiet blocker behind most stalled AI projects in finance. Not the model, not the prompt. The data. And usually not missing data, but data that doesn’t match across your systems. This edition is about what that looks like in a workflow you run every month, why you often can't fix it at the source, and the two moves that get you unstuck.
The free section shows you where these workflows break and why. The subscriber section has the two tools I used on that call, both ready to paste into Claude with your own files: a prompt that maps your workflow and finds the break, and the matching build that clears what is left.
Cohort 2 of Claude in Action is underway, and I'm opening Cohort 3. It starts September 4.
I'm holding the rate at the early promotional level, and seats are capped to keep the group small and hands-on. If you want your finance team using Claude on real work, not just trying it, this is the room for it.
Sign up here
You can also have me run Claude in Action for your team, built on your own workflows and data examples, with everything we build together staying with you after the training. Just reply to this email to get started.
When Files Will Not Line Up
When a finance team tells me their data isn't clean enough for AI, it's almost never missing data. It's data that won't line up.
This shows up in one specific kind of work. Any time you take one file and match it against another. Reconciling a payment processor against the ledger. Consolidating two entities. Matching invoices to payments. Cancelling one dataset against another to find what's left. I wrote about this a few weeks back as file-to-file transformation. Those workflows only run cleanly when the datasets share something a machine can match on. Often they don't.
And the reasons they don't go well beyond a missing ID. The same customer is one name in your payment processor and another in your ledger. Two systems record the same sale in different currencies. Reports come out in different languages across regions. Each system formats dates, names, and refunds its own way. The numbers are all correct, but nothing connects.
Here's what that looks like in a workflow most of us run every month.
Credit card revenue reconciliation
Download the transaction detail file from the registration system
Retrieve the order detail file from the registration system (it links to the first by confirmation number)
Export the deposit file from the payment processor
Pull the revenue postings from the accounting system
Match the processor deposits to the accounting postings by amount and date
Match the accounting postings to the registration orders by customer name
Verify every customer in the registration file appears in the accounting file
Verify total matched revenue equals the bank deposit for the month
Flag the leftovers: name mismatches, one order covering several registrations, refunds
Send the flagged rows to a review tab for a person to clear
The whole thing limps at one step: matching by customer name, because the accounting system stores a name and nothing else. No confirmation number. A clean, mechanical reconciliation turns into guesswork.
Here's a second one, different corner of finance, same disease.
Cash flow forecast classification
Download the current month bank transaction export
Retrieve last month's classified transactions to use as the reference set
Pull the vendor and customer master list
Assign each transaction its cash flow type: a customer receipt, a refund back to a customer, goods income, services income; each one lands in a different line of the forecast
Verify every transaction has a type, none left blank
Verify the type totals tie back to the net change in the bank balance
Flag the transactions the reference set couldn't type with confidence
Send the low-confidence rows for manual review
This one breaks at the typing step. Some transactions were typed consistently in the past and some weren't, so there's no clean rule to copy forward. That inconsistency is the "bad data" nobody puts a name to.
The obvious move is to fix it at the source. Sometimes you can. Just as often, the team already tried, and the system wouldn't let them. Here's why.
The system has no field for it. There's nowhere to put the shared key.
Each system has its own schema and export format. They were never designed to talk.
You operate across countries, and currency, language, tax treatment, date formats, and refund handling all differ.
The source files change shape month to month, so any rule you hand-build quietly breaks.
The data is already posted. You are not going back to fix thousands of historical rows.
So this is what "getting your data AI ready" actually means. Not a cleanup project you finish once. It's building the map of how your files connect, finding the exact point where the match breaks, and deciding what to fix upstream and what to hand to AI.
Back to that client call. Partway through, the team spotted a source fix: add the confirmation number into a memo field when they post the payment. Small habit, big drop in the matching problem. But not zero. There was still a second thing the records had to match on, so the exceptions came down hard and then leveled off. Better, not solved.
For everything the source fix couldn't reach, we built a project in Claude during the call. A monthly reconciliation that used to eat about two days came down to roughly 30 minutes.
None of this is exciting. Building a matching map, chasing down why two files won't line up, deciding what to fix upstream. It's the boring work, and nobody signs up for it.
But it's the step that makes AI actually work on your process. And once it's done, the AI sitting on top can be almost anything. A Claude project you run yourself each month. Or a bigger system someone builds specifically for you. The tool matters less than the map underneath it.
Seeing where your data breaks is most of the battle. Getting your two days back is the other half. Here are the two tools from that client call, written to paste into Claude with your own files. The first maps your workflow and finds the break. The second clears what the source fix can't reach.
If you are not yet a subscriber, this is a good edition to upgrade on.
Closing Thoughts
One thing before you go. Pick a single workflow that stalls every month and map where the data breaks before you try to automate any of it. Not the whole backlog. One.
Then tell me what you found. Reply and send me the workflow that fights you the most. Half of what I write here starts as a subscriber's stuck reconciliation.
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Until next Tuesday, keep balancing!
Anna Tiomina
AI-Powered CFO