Open-text feedback is where spreadsheets often start to creak. Comments, reviews, and survey responses can be rich, but they are slow to categorise consistently by hand.
AI helps because it can draft labels, themes, and summaries quickly. The important word is draft. Review and sampling still matter if you want a result you can trust.
Quick answer
Use AI to accelerate theme extraction, draft categorisation, and first-pass summarisation of text data in Excel. Keep the workflow grounded by sampling outputs, checking edge cases, and documenting how the labels were produced.
- You have too many comments to code manually in a reasonable time.
- You need themes or categories quickly for a report.
- You can still review a representative sample before relying on the output.
Where AI helps most
AI is strongest when the task is repetitive interpretation at scale: grouping similar complaints, drafting sentiment-style labels, or producing a first-pass summary of what people keep mentioning.
Why sampling is not optional
You need to know whether the labels hold up on real examples, edge cases, sarcasm, and domain-specific wording. Sampling keeps the workflow honest.
How to keep the process reviewable
Label the AI-generated columns clearly, keep the prompt or method documented, and separate raw text from interpreted outputs so you can revisit the logic later.
Worked example: post-purchase survey comments
An e-commerce team receives 4,000 survey comments in a quarter. AI helps draft categories such as delivery speed, packaging, fit, returns, and customer support. The analyst then samples each category before using it in the presentation.
Common mistakes
- Treating first-pass categories as final truth.
- Skipping sample review because the totals look tidy.
- Mixing raw comments and interpreted labels without documenting the method.
When to use something else
If the text needs to stay in a workbook but the real bottleneck is AI-generated formula safety, go to reviewing AI formulas. If the next step is turning the themes into a presentation-ready output, charts with Copilot is the closest follow-up in this batch.
Frequently asked questions
What text tasks is AI good for in Excel?
Repetitive interpretation at scale: grouping similar comments into themes, drafting sentiment-style labels, and producing a first-pass summary of what people keep mentioning across a column of feedback.
How do I categorise free-text comments?
Give the model a fixed list of categories and ask it to assign each comment to one (plus an 'other' bucket) in a new column. Defining the categories yourself keeps the labels consistent and reviewable.
Why is sampling not optional?
You need to know the labels hold up on real examples, including edge cases, sarcasm, and domain-specific wording. Spot-check a sample against your own judgement before trusting the whole column.
How do I keep the workflow reviewable?
Label the AI-generated columns clearly, keep the raw text separate from the interpreted output, and document the prompt or method so the logic can be revisited later.
Should I trust AI sentiment labels?
Treat them as a first pass. Sentiment is subjective and AI misreads sarcasm and mixed feedback, so sample and correct before you report on them.
Can I do this without sending data to the cloud?
For sensitive feedback, use a local model or your organisation's approved AI rather than a public tool, and check the data-handling rules before pasting customer text anywhere.
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