AI for Excel & Google Sheets: Stop Wasting Hours on Spreadsheets

Spreadsheet AI is unusually easy to evaluate, because the good uses and the dangerous ones separate along one clean line.

If it writes a formula and the spreadsheet calculates the result, trust the mechanism. The formula is visible, you can read it, and the arithmetic is done by the spreadsheet engine — which does not guess.

If it states a number in prose, verify it. That figure may have been predicted rather than computed, and a language model predicting a plausible number is exactly as fluent as one reporting a real one.

Nearly everything worth knowing follows from that distinction.

What It Is Genuinely Good At

Writing formulas from a description. The strongest use. You describe what you want in plain language and get a formula. This removes the specific friction of knowing the logic but not the syntax, and nested lookups or complex conditionals stop being a documentation-hunting exercise.

The reason it is safe: the formula lands in a cell where you can read it and the spreadsheet evaluates it.

Explaining a formula somebody else wrote. Inheriting a workbook with a forty-character nested expression and no documentation is a common and miserable situation. Pasting it in and asking what it does is a genuine improvement over reverse-engineering it by hand.

Cleaning inconsistent data. Dates entered five ways, names with stray whitespace, mixed capitalisation, text where numbers should be. Describing the target state and getting the transformation is fast and checkable.

Suggesting analysis and charts. Useful orientation on an unfamiliar sheet — what is here, what might be worth looking at.

Drafting the narrative. Once numbers are verified, turning them into a written summary for a report is transformation work, which is where these tools are reliable.

Where To Be Careful

Prose numbers. Covered above and worth repeating because it is the actual risk. Ask for a formula rather than an answer wherever you can.

Silent handling of missing data. Blanks, text in numeric columns, and duplicates all get handled somehow, usually without comment. A formula that quietly ignores blanks produces a different average from one that treats them as zero, and both look correct.

Formulas that are valid but wrong. It will confidently produce something that calculates cleanly and answers a slightly different question from the one you asked. This is the most common failure and the hardest to spot, because nothing errors.

Large or messy workbooks. Assistants work from a limited view of your data. On a sheet with many tabs and interdependencies, they may reason from a fragment while sounding fully informed.

The Habit That Prevents Most Problems

Sanity-check every generated formula against a small case you can verify by hand.

Take three or four rows where you already know the answer. Run the formula. Compare. It takes a minute and it catches the entire category of valid-but-wrong results, which is the failure mode that actually costs people.

This matters more in spreadsheets than almost anywhere else, because errors here propagate. A wrong figure feeds every dependent cell, the chart, the summary tab and the report, and nothing anywhere flags it. Unlike a broken formula, a wrong-but-working one is invisible until someone acts on it.

The underlying reason these systems cannot warn you is covered in how AI actually works, and the general pattern in why AI fails.

Which Ecosystem

Both major spreadsheet products now have assistants integrated directly, and both do broadly the same things: natural-language formula generation, explanation, cleaning, chart suggestions, and summarisation.

Practically, use whichever you are already in. The differences are smaller than the difference between using an assistant and not, and neither is worth migrating a workflow for.

On packaging: the deeper assistant features have generally sat behind business or add-on tiers while lighter features appear in standard plans, and both vendors have reorganised this more than once. Check what your current plan actually includes rather than trusting any published breakdown, including this one.

Third-party add-ons exist and are mostly worth skipping now that the native features cover the common cases.

What This Does Not Change

The skill that matters is not writing formulas. It is knowing which calculation answers the question, and recognising when a result is implausible.

Generating a formula was never the hard part for anyone competent, and it is now easy for everyone. What remains hard — and becomes more valuable — is structuring the analysis correctly and noticing when a number is wrong. Someone who cannot evaluate the output cannot safely use the tool, which is the same conclusion reached in AI for data analysis and it applies with more force here because spreadsheets carry decisions.

Getting Value Quickly

Start with formula generation on something you can verify. Then explanation, on a workbook you inherited. Then cleaning, on a dataset you can eyeball.

Keep the checking habit through all of it. Small verifiable case, compare, then trust.

For analysis beyond what a spreadsheet handles well, AI for data analysis covers the tooling and the checks. For fitting this into wider process work, AI for business automation covers where reporting sits, and the small business AI guide covers what to prioritise when nobody has time for any of it.