Your invoices know which months actually carry your year. Your expense export knows which subscriptions quietly renewed. Your customer list knows which neighborhoods send you work and which ones never do.
None of that used to be worth the afternoon it took to find out, so most owners never found out. That has changed. You export a file, attach it to Claude or ChatGPT, and ask the question in plain English. No pivot tables, no formulas, no bookkeeper.
This is a working session, not a think piece. Four exports, five questions worth asking, and the exact prompt for each one. Everything here works with a free AI account and files you already have.
Time needed: about 10 minutes for your first export and answer. About 40 minutes to work through all five.
Jump to what you need
How this actually works
There is no setup and nothing to install. The whole loop is three steps, and the middle one is the only part people get wrong.
You need a file, not an integration
You are not connecting your accounting software to anything. You export a file, attach it to the chat the same way you would attach it to an email, and delete the chat when you are done if you want to. Nothing is stored in your books, and nothing changes in QuickBooks.
The four exports worth doing
You almost certainly already have all four. Start with whichever question is bothering you most, and do exactly one. You can come back for the others.
1. Your customer list with addresses
What it is: your customers with their city and zip, and ideally the date each one became a customer.
Answers: where your customers actually come from, which areas are growing, and which nearby neighborhoods send you almost nothing. That last one is usually the cheapest advertising you will ever buy.
2. Your invoicing or sales history
What it is: every sale with dates, amounts, customer, and payment status.
Answers: your peak months and what was different about them, your highest-spending clients, and which invoices consistently take longest to get paid.
3. Your line-item revenue
What it is: not just totals, but every individual product or service sold, with its description.
Answers: which service lines actually carry the business. This is the one that surprises people. AI will read a thousand messy line descriptions written by four different people and sort them into real revenue channels, which is exactly the job a spreadsheet cannot do.
4. Your expenses
What it is: everything you spent, with dates, vendors and amounts. Twelve months if you can get it.
Answers: where money is leaking. Expenses grow quietly, which is what makes them hard to see. The usual culprit is subscriptions signed up for during a busy month and never cancelled.
The five numbers every owner should know
Each one below gives you the decision it drives, the data to feed the AI, and a prompt you can copy as written. The prompts deliberately ask the AI to show its working and flag anything that looks wrong, because a confident wrong answer is worse than no answer.
Where your customers actually come from
- The decision
- Where to spend advertising money, and which areas to stop paying for.
- Data to attach
- Customer list with city and zip. Include the acquisition date if you have it. Delete names and emails first.
Using the attached customer address list, analyze the data to identify the top 5 cities and top 10 zip codes where my customers are located. For each, tell me the number of customers and their percentage of the total customer base. If customer acquisition dates are available, also identify if there are any emerging geographic trends over the last year. Show your calculations and flag any data points that seem incomplete or inconsistent.
What you truly earn per service, not just revenue
- The decision
- Which services to sell harder, reprice, or quietly stop offering.
- Data to attach
- Line-item sales for 12 months, plus your direct costs if you track them separately.
I have attached a line-item sales export and, if separate, a corresponding direct cost sheet. Please calculate the gross profit margin for each service or product line over the last 12 months. Identify the top 3 most profitable services/products and the bottom 3. Provide the total revenue, total direct costs, and gross profit margin percentage for each. Show your working for how margins were calculated and highlight any service lines where cost data appears missing or unusually high/low.
Revenue and profit are not the same thing, and the gap between them is where most pricing mistakes live. Your busiest service is not always your best one.
How exposed you are if one client leaves
- The decision
- Whether you need to go find more customers before something forces you to.
- Data to attach
- Revenue by customer for the last 12 months. Customer names can be replaced with Client A, Client B if you prefer.
Using the attached 12-month revenue-by-customer data, identify my top 10 highest-revenue customers. For each of these top 10, state the total revenue generated and their percentage contribution to my overall revenue. Additionally, calculate the percentage of total revenue that these top 10 customers represent combined. Explain how these calculations were performed and note any customer entries that appear to be duplicates or have unusually sporadic revenue patterns.
If one client is more than a quarter of your revenue, that is not automatically a problem. It is something you should know on purpose, rather than find out in an email one Tuesday.
Where money is leaking
- The decision
- What to cancel this week.
- Data to attach
- Twelve months of expenses with vendor and amount.
Analyze the attached 12-month expense export. Identify the top 5 expense categories by total spend. Within these categories, list any vendors where spending has increased by more than 15% in the last 6 months compared to the prior 6 months. Also, identify any recurring subscriptions that appear to be duplicated or have had price increases. Show the amounts and percentage changes for any identified increases and flag any transactions categorized as "undefined" or uncategorized.
Whether next quarter is safe
- The decision
- Whether you can hire, or whether you need to hold cash.
- Data to attach
- Monthly revenue and expense totals for 24 months. This one needs no personal data at all, just two columns of numbers and a date.
Using the attached 24 months of monthly aggregated revenue and expense data, analyze the trends. Identify any clear seasonal patterns in both revenue and expenses. Based on these patterns and the most recent 6 months of data, provide a projected revenue and expense estimate for the next three months (the upcoming quarter). State any assumptions made in the projection and flag any months with unusually high or low figures that might skew the overall trend.
Treat the projection as a conversation, not a forecast. AI is good at spotting the seasonal shape of your business and bad at knowing that you lost a big client in March. Read the assumptions it states, and correct the ones that are wrong.
What to redact before you upload
This is not the alarmist section. It is thirty seconds of work that makes the whole thing safe, and the good news underneath it is this: almost every analysis above works fine on data with the personal columns deleted.
Keep, the analysis needs it
- Dates
- Amounts and quantities
- City, state, zip
- Product or service description
- Vendor or category name
- A customer reference like Client A
Delete, it changes nothing
- Full names
- Street addresses
- Email addresses and phone numbers
- Card and bank account numbers
- Social security or tax ID numbers
- Notes fields, which hide surprises
For a geography question you need the zip code, not the person. For a margin question you need the amount, not who paid it. Deleting a column in a spreadsheet takes one click, and it removes almost all of the risk.
Before your first upload, tick these off
Does my industry have extra rules?
Healthcare, dental, therapy, anything touching patient records
Yes, and this is the strictest case. Patient information is covered by HIPAA, and a general consumer AI plan is very unlikely to be covered by a Business Associate Agreement. Do not upload anything that identifies a patient. Aggregate financial data with no patient identifiers is a different matter, but confirm it with whoever handles your compliance before you start.
Mortgage, lending, insurance, financial services
Financial data carries its own rules, and borrower information is sensitive by default. Strip names, account numbers and anything that identifies an individual. Aggregate revenue, expense and volume analysis is usually fine once identifiers are gone. If you operate under a broker or carrier agreement, check what it says about third-party services.
Any business with a client confidentiality agreement
Worth actually re-reading. Some agreements restrict sending client data to third-party services, and an AI tool is a third-party service. This often catches agencies, consultants and contractors by surprise. Anonymizing the client, so Acme Corp becomes Client A, usually resolves it, but the agreement is the authority, not this article.
Everyone else, which is most small businesses
No special regime applies to you. Delete the personal columns out of good habit, check your plan's training setting once, and get on with it. The analysis works the same either way.
One thing worth knowing about plans
Business and enterprise AI plans generally carry stronger data commitments than personal ones, including agreements about not training on your data. Personal plans often have a training setting you can switch off yourself. Either way, look it up for your specific plan rather than assuming, and do it once rather than worrying about it every time.
Your checklist
If you only do one, do the expenses. It is the fastest to run and it usually pays for itself the same afternoon.
Want to run this on your own numbers with help?
We run a free workshop every Friday at 2 PM Eastern. Bring one export and we will work through it live. No pitch, and you never have to share your screen if you would rather not.