The Secret AI Power Hiding Inside Google Sheets

By Dan Sanchez · Episode published

AI in Google Sheets can complete structured tables, classify survey responses, and reconcile metrics across browser tabs. The key is knowing what data it can trust and verify.

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Most marketers use spreadsheets to organize information, calculate results, and build reports. The newer opportunity is to use AI inside the sheet to do the thinking work that sits between raw information and a useful decision.

Google Sheets can now help complete a table, classify qualitative responses, and pull together information from several browser tabs. The feature is powerful, but it works best when you understand what the model knows and what it still needs to look up.

Let the existing pattern do the work

The simplest use case is completing a structured table. Put the categories in columns, the items in rows, and fill in a few examples. Then select the empty cells and ask Gemini in Sheets to complete the table.

I tested this with a study sheet about spiritual gifts. The columns described the gift, its meaning, and how it could appear naturally and spiritually. After I filled in roughly half the rows, Gemini completed the rest in the same format. It was not calculating anything. It recognized the pattern and generated the missing information.

That can save a marketer from repetitive research and formatting. A content calendar, campaign inventory, competitive comparison, or customer profile table can all benefit when the structure and examples are already clear.

The important limitation is source knowledge. When I asked the same kind of sheet to find current company executives, LinkedIn profiles, and recent funding announcements with no supporting data, it hallucinated the dataset. A model can complete a pattern from information it knows, but a current research task needs a retrieval source, links, and verification.

Turn open-ended answers into useful categories

AI becomes even more useful when a spreadsheet contains qualitative text. Suppose a survey asks, “How did you hear about us?” and people answer in their own words. A keyword filter will miss the meaning in many responses.

Add a neighboring column and ask Gemini to classify each response into a defined set of categories, with an “other” option when nothing fits. It can read the original sentence, choose the category, and preserve the raw response beside the new label. You can then count the categories, build a chart, and still return to the source language when you need the detail.

This is a practical bridge from qualitative research to quantitative reporting. It does not erase the original data or pretend the categories are objective truth. It gives you a first pass that makes a large set of responses easier to analyze.

Reconcile the tabs you already have open

Another useful workflow happens in the browser rather than a single cell. Open the dashboards or reports you need, then ask Gemini to gather the same metric from each tab and create a consolidated sheet.

For a podcast, that might mean collecting episode views from YouTube, Spotify, and the hosting platform, placing each source in its own column, and calculating the total. The same approach can help with social reports, email performance, or several business-unit dashboards.

The output still deserves a spot check. Compare several rows against the original tabs before you use the report in a meeting. The value is that Gemini removes the repetitive collection and formatting, leaving you more time to interpret the pattern and decide what to do.

Use the right tool for the right source

The practical skill is not simply knowing that AI exists in Sheets. It is knowing whether the answer should come from the model’s training data, a live web search, or a connected system such as Clay. Stable background information may be fine for pattern completion. Current company data, funding, prices, and performance numbers need a source that can retrieve them.

Once you know that distinction, spreadsheets become more than storage. They become a workspace where AI can organize, classify, reconcile, and prepare information for judgment. The marketer still decides what the categories mean, which numbers are credible, and what action follows. AI simply makes the path from messy data to a useful first draft much shorter.