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Seven patterns for replacing repetitive Excel work with AI

You do not need to move all of your spreadsheet work to AI. Leave what formulas already do well, and hand over only the part a person was eyeballing and judging. Here are the seven patterns that keep coming up in practice.

7 patterns AI vs formula Verification included

The short answer

The most common failure in spreadsheet automation is letting AI do the arithmetic. Formulas are exact and reproducible. What AI is good at is not calculating but organising, classifying and interpreting — the part a person used to do by looking.

The seven below are split along that boundary, and each one states how you verify the result. Automation without a verification step is not automation, it is delegation — and when it is wrong, nobody finds out.

Draw the boundary first — what to hand over, and what never to

Task Give it to Why
Totals, ratios, deltas A formula The result is always the same, and it can be audited
Branching on a condition A formula IF and VLOOKUP are already exact and fast
Classifying free text AI It handles variations in wording that no rule set covers
Pulling fields out of unstructured documents AI The layout changes every time, so no formula can locate anything
Summarising what a table means AI The output needed is prose, not a number
Final amounts and reported figures A formula (never AI) A value you cannot walk back has to be reproducible

The seven patterns

1. Classify free-text answers — Survey comments, enquiry text, reviews — a column someone used to read and tag. Verify: re-check 20 of every 100 by hand and record the agreement rate. Below 90%, rewrite the category definitions before anything else.
2. Normalise names and spellings — The same customer stored three different ways. AI produces a mapping table to one canonical form. Verify: a person approves the mapping table before it touches the source. Never let it edit the source directly.
3. Unstructured document to table — Pull line items out of invoices, receipts and quotes into rows. Verify: compare your total against the total printed on the original. If they differ, that document goes to a human.
4. Reconcile two tables — Stock against orders, payments against invoices — two tables whose keys do not match exactly. Verify: automate the certain matches, and collect the ambiguous candidates in a "needs review" list. Do not let AI decide those.
5. Flag anomalies — Find sharp month-on-month moves, blanks and format errors, and list them with candidate explanations. Verify: a person checks only the flagged rows. AI does not fix them.
6. Draft the report sentences — Write the summary prose from a table that is already final. Verify: every number in the prose must match a value in the table. If a number appears that is not in the table, discard the sentence.
7. Turn the repetition itself into a script — Once you are running steps 1–6 by hand every month, that is the moment to move them into a workflow tool or a script. Verify: re-run last month through the new script and confirm you get the same values.

Keeping the order reduces the failures

There is one thing to do before choosing a pattern: write down how many times a month you do the task and how many minutes it takes. Without that number you have no way to judge whether the automation worked, and nothing to put in front of a budget holder.

Scope comes next. Make the first attempt one column, one task. Move a whole sheet at once and finding where it went wrong costs more than the automation saved.

Last is preserving the original. Always write AI output to a new column or a new sheet. Automation that overwrites source values cannot be undone, and what cannot be undone cannot be verified.

Frequently asked questions

Can I use AI inside the spreadsheet itself?

There are AI features built into the spreadsheet tools, and there is sending the file out to an external tool. If company data is involved, the deciding factor is not features but data handling: check first whether what you paste is used for training, and where it is stored.

The results vary slightly each run. Is that usable?

Anything involving judgement — classification, summarisation — will vary. That is why it never touches final figures, and why a human check is attached. Work that must return the same value every time belongs to formulas in the first place.

What about files containing customer data?

That decision comes before the choice of tool. For files holding personal data or commercial terms, confirm the storage location and the retraining position in the contract before uploading. Until that is settled, test on a sample with the identifying fields stripped.

Which pattern should we start with?

Pattern 2, normalising spellings, carries the least risk. The output is a mapping table a person can review at a glance, and it never touches the source.

Start with what to hand over

Decide which task moves first

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