Finance and admin
Ask your sales data questions in plain language
Pick one trusted source of sales data, such as your e-shop, POS or ERP export. Clean it and write down what each column and key term means, like "net sales" or "returning customer". Then use an AI tool that can read that data and answer plain questions with a table or chart. Check its first answers against reports you already trust.
Effort:
Medium
Time saved:
Roughly 1–3 hours a week of report requests; measure it
The problem today
Owners and managers have questions about sales every week, but the answer depends on the one person who knows how to build the report. Large retailers already give store and category managers AI tools that answer sales questions in everyday language. For a Greek SME the affordable version is a clean sales file or the built-in AI of your e-shop or reporting tool, with agreed definitions.
How it works, in plain words
It is like having the colleague who builds your reports next to you, answering a plain question with a table.
What goes in: one clean export from your e-shop, POS or ERP, without customer names, plus a short data dictionary explaining each column and how you count net sales, returns and VAT.
What the AI does: it turns your question into a calculation on that data, for example this month's sales by store against the same month last year, and shows the table or chart behind the answer.
What a person checks: a person compares the first answers with reports you trust and checks any figure before it is used in a decision.
What comes out: quick answers, tables and simple charts for weekly meetings, buying and campaign planning.
It only knows the data you give it and can misread a column, so it does not replace your accounts.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A bakery chain with five shops in Serres
The owner wants to know which products sell out early in each shop, but the POS reports need an hour of spreadsheet work. The chain would export daily POS sales, write a one-page data dictionary and ask AI questions like 'which items sold out before noon last week?'. It would check the first month of answers against the POS reports and measure the hours saved on weekly reporting, as an estimate.
A children's toy e-shop in Agrinio
Before ordering for Christmas, the two owners need to know which categories and age groups grew this year. They would export two years of order lines without customer details and ask for sales by category and month, with the table shown each time. A realistic first target could be a buying plan built from checked figures in an afternoon instead of several days.
A 4-person in-house marketing team at a skincare brand in Athens
The team wants to see which campaigns brought sales, but depends on the finance team for every report. It would combine e-shop order data with campaign dates and ask AI to compare sales before, during and after each campaign. It would track how often marketing decisions are backed by a checked sales table.
How to do it, step by step
List the 15–20 sales questions you ask most often, such as best sellers by store, sales versus last year, average basket value or products with falling sales.
Choose one source and export or connect it: e-shop, POS or ERP. Remove columns you do not need, especially customer names and contact details.
Write a short data dictionary: what each column means, how you count net sales, returns and VAT, and which date counts. Give it to the AI tool as standing instructions.
Ask your questions and require the AI to show the table or calculation behind each answer, not just a sentence. A confident sentence can still be wrong (see AI hallucination).
Compare the first answers with reports you already trust. Where they differ, fix the definitions or the data, not only the wording of the question.
Open it to 2–3 managers with a short guide of good questions, and refresh the data on a fixed schedule. For one-off files, see AI for spreadsheet and report analysis.
Tools to consider
Shopify Sidekick: answers plain questions about your Shopify store's sales, products and customers. Caveat: it only sees Shopify data, so physical stores and other channels are missing.
Microsoft Power BI with Copilot: answers questions and builds charts on top of your existing reports. Caveat: someone must first set up a clean data model, because Copilot works only on what is there.
ChatGPT data analysis: upload a sales export, ask questions and get calculations and charts. Caveat: it works on a snapshot file, not live data, and you must check the results.
Gemini in Google Sheets: answers questions about sales data kept in Sheets. Caveat: large or messy sheets give weaker answers.
Data protection and GDPR
Sales data often includes customer names, emails, loyalty IDs and purchase history, which are personal data. Most questions do not need them, so remove or replace identifiers before loading data into an AI tool. Use business plans with a data processing agreement and training switched off. Control who can see what: a store manager may not need company-wide margins or customer-level details. If you analyse individual customer behaviour at scale, a DPIA may be needed. Check with your DPO or legal advisor.
Mistakes to avoid
Loading raw data with customer details when totals by product or store would do.
Skipping the definitions, so the AI counts "sales" differently from your accountant.
Acting on a surprising number without checking the table behind it.