Finance and admin
AI for spreadsheet and report analysis
Clean the spreadsheet first: one header row, clear column names, no merged cells. Then ask an AI assistant specific questions, such as which products lost margin this quarter or which customers pay late. Ask it to show the formula or steps it used, and check key numbers yourself. Use it to draft the commentary for monthly reports, not to make the decisions.
Effort:
Low
Time saved:
Roughly 2–5 hours a month per report; measure it in a pilot
The problem today
Managers receive exports from the ERP, the e-shop and the bank but have no time to analyse them. Reports end up as tables without explanation, and important changes are spotted too late.
How it works, in plain words
It is like having an analyst sit next to you, read your export and answer one clear question at a time, showing the working.
What goes in: a tidy export from your ERP, e-shop or bank, with one header row and clear column names, and personal details the question does not need removed.
What the AI does: it runs the calculation you ask for, such as comparing this quarter's margin by category with last quarter's, and explains the steps or gives the formula.
What a person checks: you check two or three key numbers by hand or with a pivot table. If they differ, your check wins.
What comes out: answers, reusable formulas and a short plain-language commentary for your monthly report.
It does not know your business context or make decisions. Messy sheets and vague questions give confident but wrong answers.
How it could work for businesses like yours
Illustrative examples, not client case studies.
A clothing retailer with 3 stores in Tripoli
The owner gets weekly sales exports from the POS but rarely has time to compare stores or seasons. They would clean one export, ask which categories lost margin this season in each store, and ask for the formula to reuse next month. A realistic first target could be a one-page monthly summary prepared in under an hour.
A 3-person marketing team inside a 70-person dairy company in Ioannina
The team receives campaign reports and retail sell-out data in separate files and writes the monthly review by hand. They would combine the two in one tidy sheet, ask AI to compare sales in weeks with and without promotions, and check the key figures in a pivot table. They would measure time spent on the monthly review.
An 18-person engineering consultancy in Kozani
Project hours are logged in a spreadsheet, but no one compares them with budgets until a project closes. The firm would ask AI each month which projects have used more than 80% of their hours with work still open, and draft a short note for the partners. They would track how early overruns are spotted.
How to do it, step by step
Prepare the data: one table per sheet, clear headers, consistent dates and currency. Remove names, IBANs and other personal details the question does not need.
Write down the 3–5 questions you want answered every month, for example margin by category, the largest late payers, or costs that grew by more than 10%.
Ask one question at a time and ask the AI to show its calculation. In Excel or Sheets, ask for the formula itself so you can reuse it without the AI.
Check two or three numbers by hand or with a pivot table. If the AI's answer and your check differ, trust the check and find out why.
Ask for a short commentary in plain language: what changed, possible reasons, what to look at next. Treat the possible reasons as hypotheses to confirm with your team.
Turn the prompts and formulas that worked into a monthly template, so next month's report takes less time.
Tools to consider
Microsoft 365 Copilot in Excel: analyses tables and suggests formulas and charts inside your workbook. Caveat: it works best on tidy tables stored in OneDrive or SharePoint; messy sheets give poor answers.
Gemini in Google Sheets: helps create formulas, summaries and tables in Sheets. Caveat: features vary by Workspace plan, so check every formula it writes.
ChatGPT data analysis: runs real calculations on uploaded files and explains them step by step. Caveat: upload only anonymised data on a business plan, and still verify the results.
Claude: good at reading longer reports and drafting clear commentary. Caveat: for heavy calculations, confirm the numbers in your own spreadsheet.
Data protection and GDPR
Financial spreadsheets often contain personal data: customer names, employee salaries, bank details. Remove or replace them before uploading whenever the question does not need them. Use business versions of AI tools with a data processing agreement and no training on your inputs. Never upload payroll or HR files to a free chatbot. Margins and supplier prices are not personal data but still deserve protection. Using AI to score the creditworthiness of individual people is a high-risk use under the AI Act, so keep decisions about individual customers with a person and check with your DPO or legal advisor.
Mistakes to avoid
Trusting totals and percentages without checking a few by hand.
Uploading full ledgers or payroll files when an anonymised extract would do.
Asking vague questions like "analyse this" instead of specific business questions.