How to analyze Excel files with AI

To analyze an Excel file with AI, upload a structured workbook or use the configured Excel add-in with a selected table or range. Ask a question in plain English, then check the selected sheets, relationships, calculations, and chart against your source data.

What "analyzing Excel with AI" means

The phrase gets used loosely. Sometimes it means pasting a screenshot of a sheet into a chatbot and asking it to describe what's there. That's summarization — a fine way to get oriented, but it isn't analysis, and it won't produce a number you can put in a meeting and defend.

Real analysis means the system runs an actual computation against your data — a sum, an average, a join, a trend — and shows you the result. With tableArth.ai, your plain-English question is turned into a query, the query runs against your workbook, and what comes back is a computed answer plus a chart, not a paraphrase of what a screenshot looked like. The rest of this guide walks through how that works, from connecting the file to asking one question across several of them at once.

Connect the file, don't just upload it

Choose how to provide your Excel data. An uploaded workbook is a snapshot; upload an updated version when it changes. The Excel add-in works with selected tables or ranges and requires installation and activation. Confirm the selected data before starting another analysis.

• Excel uploads remain snapshots until you provide updated data. In the configured add-in, select the current table or range for analysis.
• Uploaded Excel is a snapshot — fine for a one-off look at a file you won't touch again, but it won't update itself.

For anything you'll come back to — a weekly expense sheet, a sales pipeline, a budget that gets edited daily — connect it. You can see every source tableArth.ai connects to, including Google Sheets and databases alongside Excel, on the data sources page. For the fuller case on why live beats a snapshot, see live data vs. one-time uploads.

Multi-sheet workbooks, read as related tables

Real Excel files are rarely one flat sheet. There's an Orders tab, a Customers tab, a Vendors tab — each with its own columns, held together by a VLOOKUP or two. tableArth.ai reads a multi-sheet workbook as a set of related tables instead of flattening it into one blob first. Each tab keeps its own structure, and you can ask a question that spans several of them.

Ask something that touches two tabs — total spend by vendor category, say, where "spend" lives on one sheet and "category" lives on another — and tableArth.ai works out how the tabs relate and joins them on the shared key automatically. That's the same join it would run across tables in a database; it just also understands it inside a single Excel file. Nobody has to combine sheets by hand first, and nobody has to write or maintain a VLOOKUP to keep the join working as rows are added. For a closer look at how the joins themselves work, see joining data across sheets and tables.

The questions you can actually ask

Once a workbook is connected, the interface is a question box, not a formula bar. Ask something like "what were our top five customers by revenue last quarter," and tableArth.ai writes the query behind it, runs it against the right tabs, and streams back a written answer — as analysis completes — with the chart that fits: a bar chart for a comparison, a line for a trend, a table when you need the raw rows, and five more chart types besides. If the first query the engine writes doesn't run cleanly, it self-corrects and retries up to three times before you ever see an error.

app.tablearth.ai / Q3 Expenses.xlsx

Q3 Expenses.xlsx

Ask Sheets

You Combine the Expenses and Vendors tabs — total spend by category this quarter?

tableArth.ai · joined 2 tabs · 3.1s

Software & Tools leads spend at $86,400, followed by Travel at $54,200 and Professional Services at $38,900. Figures join the Expenses tab with vendor categories from the Vendors tab — no VLOOKUP required.

Spend by category Q3 · USD

SoftwareTravelProf. Serv. MarketingOfficeOther

Illustrative product preview — sample data.

If you don't know where to start, you don't have to guess. tableArth.ai suggests relevant questions for the workbook you've connected, so there's always a strong starting point instead of a blank box. And when a question is genuinely ambiguous — "best customers" could mean by revenue, by retention, or by order count — it asks a quick clarifying question instead of picking an interpretation for you, so you get to the right answer with more confidence and fewer wasted round trips. We go deeper on that behavior in how tableArth.ai asks clarifying questions.

Large files, handled

Big Excel workbooks — years of transactions, a full general ledger, tens of thousands of rows spread across several tabs — used to be where uploads got flaky. That's fixed: the timeout issues that affected larger uploads are resolved, so the upload process is more reliable and smoother for bigger files. You shouldn't have to trim a workbook down or split it into pieces just to get it in the door.

Beyond one file: Workbooks

A single Excel file answers a lot of questions, but real analysis usually needs more than one source — the Excel budget, a Google Sheet the sales team owns, and a production database. A tableArth.ai Workbook is where those connect together: Excel files, Google Sheets, and databases such as MySQL and MongoDB, side by side in one place, with more connectors on the way.

A workbook retains the access and refresh behavior of each source. Combining Excel with a database requires compatible query paths, matching fields, and aligned periods. Answer privacy modes control the answer stage; planning and query generation may still use a model. Review the security guide before selecting a mode.