Google Sheets’ AI Function: Turn Customer Feedback Into Reviewable Categories

A spreadsheet full of customer comments can reveal repeated problems, but inconsistent labels make those patterns difficult to count. A useful starting point is to let AI suggest categories, review those suggestions, then count only the approved labels.

Google documents text generation, summarisation and categorisation through the Sheets AI function. Access requires an eligible Google Workspace or Google AI plan; check your account before building this workflow around it. Google’s AI function guide.

This proposed workflow is for small-business owners and support teams. It has not been hands-on tested and makes no accuracy or time-saving guarantee.

Start with a small sample

Choose 20 representative comments. Use fictional examples or information you are authorised to process, removing unnecessary names, email addresses and order details. Check your organisation’s data-handling rules before using real customer records.

Create four columns:

Pick categories that lead to useful action. For a hypothetical online store, these could be Delivery, Product quality, Returns, Website usability and Other. Write a short definition for each so reviewers apply the same rules.

Ask for one consistent label

If the comment is in B2, enter this formula in C2:

=AI("Classify this comment as Delivery, Product quality, Returns, Website usability, or Other. Return exactly one label. Use Other when unclear or when several categories apply.", B2)

Select the formula cell, then choose Generate and Insert. Keep the category spelling consistent when using the prompt for the rest of your sample. This is a suggested prompt, not a demonstrated result.

Supply the relevant cell or range as context rather than assuming the function sees unrelated sheet content or Drive files. Google’s syntax and context guidance.

Review before expanding

Read all 20 comments beside their suggested categories. Put your final decision in column D and retain column C for comparison. Check unexpected labels rather than counting them as valid categories.

Consider a fictional comment: “The replacement arrived broken.” It could concern both delivery and product quality. Under the suggested prompt, Other is the intended fallback; a reviewer may still need to investigate. If mixed comments are common, change your category rules or design a multi-label process instead of forcing misleading single labels.

Record corrections, reasons and review time in a separate review log. Expand only if the process proves more useful than manual categorisation for your team. Keep the original sample and prompt so a later change can be compared against the same comments.

Count approved categories

Select your data, including the headers, and choose Insert → Pivot table. Add Approved category under Rows and Comment ID under Values, using a count rather than a sum. Exclude blank approved categories so unreviewed comments are not treated as decisions. Google documents pivot-table creation and field configuration in its pivot-table guide.

Use one nonblank ID per comment and check that the total matches the number of reviewed rows. This counts comments, not unique customers or proven causes.

A frequent category is a reason to investigate. Read the underlying comments before changing a product, policy or support process.

Keep batches manageable

Google currently documents a cap of 350 selected AI cells in one generation batch, alongside other generation limits. This is not a daily allowance or a guarantee that your account can generate every batch. Smaller groups are easier to review. Recheck the current limits before expanding.

If you cannot access the function, you can still use this four-column structure: label the comments manually and leave the AI category column empty. The review record and approved-category count remain useful without AI.

Questions before you use this workflow

Does AI decide the final category?

No. In this workflow, its output is a suggestion. The reviewer writes the approved category, and only that column feeds the final count.

Does a high count prove the main cause of a complaint?

No. Counts show patterns in the comments you selected. Sample bias, repeated comments and mixed issues can affect the result. Investigate the underlying records before making a decision.

What should I check in the trial?

Check whether labels follow your definitions, how often you correct them and how long the complete review takes. Compare the process with manual categorisation instead of assuming AI is faster.

Documentation checked on 10 October 2026. No hands-on results claimed.