How to Use the AI Function in Google Sheets

The =AI() function in Google Sheets brings large language model capabilities directly into your cells. Instead of switching between a chatbot and your spreadsheet, you can write a prompt in a formula, reference your data, and get AI-generated results inline. This guide walks through the setup, syntax, and practical use cases so you can start using the function in your own sheets today.

What the AI Function Does

The =AI() function sends a text prompt to Google's Gemini model and returns the result in a cell. It works like any other Sheets formula: you type it into a cell, press Enter, and the output appears. The difference is that the output comes from a language model rather than a mathematical calculation.

The function supports five core operations: generating text, summarizing content, categorizing data, analyzing sentiment, and extracting structured information from unstructured text. It can also pull real-time information from Google Search when needed.

Requirements and Access

The AI function is available on the following Google Workspace plans: Business Standard, Business Plus, Enterprise Starter, Enterprise Standard, Enterprise Plus, and Education Plus. It is also available through the Google AI Pro add-on for personal Google accounts at $19.99 per month.

If you are on a free Google account without the AI Pro add-on, the function will not appear. You can check your plan at admin.google.com or look for the Gemini icon in your Sheets toolbar to confirm access.

Basic Syntax

The function follows this structure:

=AI(prompt, [range])

The first argument is your instruction enclosed in double quotes. The second argument is optional and references a cell or range containing the data you want the AI to process.

A simple example:

=AI("Write a one-sentence product description for a wireless mouse")

A data-aware example:

=AI("Categorize this customer feedback as positive, negative, or neutral", A2)

In the second case, cell A2 contains the feedback text. The AI reads it and returns a category label.

Five Practical Use Cases

The following examples show how to apply the function to common business data tasks. Each one can be dragged down a column to process an entire dataset row by row.

Categorizing survey responses. Suppose column A contains open-ended survey responses. In column B, you can write:

=AI("Classify this response into one of these categories: product quality, shipping, customer service, pricing, other", A2)

Drag the formula down to apply it to every row. The AI reads each response and assigns a category. This replaces what would otherwise require manual tagging or a custom classification script. According to Google's documentation, the function processes up to 350 cells per batch, so for larger datasets you may need to apply the formula in chunks.

Extracting structured data from text. If you receive unstructured data like company names, dates, or addresses embedded in free-text fields, the AI function can pull them out:

=AI("Extract the company name from this text", A2)

This is useful when importing data from emails, PDFs, or CRM notes where information is not consistently formatted.

Sentiment analysis on reviews. For product reviews or NPS comments, you can score sentiment directly:

=AI("Rate the sentiment of this text on a scale of 1 to 5, where 1 is very negative and 5 is very positive. Return only the number.", A2)

Adding "Return only the number" keeps the output clean and lets you run averages or build charts on the results column.

Summarizing long text entries. When cells contain paragraphs of text such as meeting notes or support tickets, you can condense them:

=AI("Summarize this text in one sentence", A2)

This is particularly helpful when preparing reports from raw data that includes lengthy descriptions or free-form comments.

Generating content from data. You can use the function to create content based on existing structured data. If column A has product names and column B has key features:

=AI("Write a 20-word marketing tagline for this product based on its features", A2, B2)

This turns structured data into copy without switching to another tool.

Prompting Tips for Consistent Results

The quality of the output depends heavily on how you write your prompt. Here are patterns that produce reliable results across hundreds of rows.

Specify the output format explicitly. Telling the AI to "return only a number" or "respond with one word: positive, negative, or neutral" prevents verbose answers that break your spreadsheet layout. Without format constraints, the model may return full sentences that are difficult to use in downstream formulas.

Include examples in the prompt. You can write: "Classify this as bug report, feature request, or question. For example, 'the app crashes on startup' is a bug report." This technique, known as few-shot prompting, improves classification accuracy by giving the model concrete reference points.

Limit the scope of each prompt. Broad prompts like "analyze this data" produce inconsistent results. Narrow prompts like "identify the primary complaint in this support ticket" work better because the model has a clear objective.

Use the range argument instead of pasting data into the prompt string. Referencing cells keeps formulas clean, makes them reusable when you drag them down a column, and avoids hitting character limits on long text values.

Limitations Worth Understanding

The AI function has several constraints to keep in mind before building workflows around it.

It cannot access your entire spreadsheet. The function only sees the cells you explicitly reference in the formula. If your analysis requires context from multiple sheets or tabs, you need to consolidate the relevant data into the referenced range first.

Outputs are non-deterministic. Running the same prompt twice may produce slightly different wording. For tasks where consistency matters, such as categorization, use constrained prompts that force specific output values from a closed list.

There is a 350-cell batch limit. Applying the function to thousands of rows at once will trigger rate limits. Processing data in batches of 200 to 300 rows is more reliable.

Processing time scales with volume. A single cell returns results in one to three seconds. Hundreds of cells may take several minutes to fully populate, and the sheet can feel sluggish while processing.

Usage counts against your Workspace AI quota. Heavy use across an organization can exhaust shared limits, so coordinate with your IT admin if deploying the function at scale.

Combining AI with Traditional Formulas

One of the most practical patterns is chaining the AI function with standard Sheets formulas. For example:

=IF(AI("Is this feedback negative? Answer yes or no", A2)="yes", "Needs follow-up", "OK")

This uses the AI output as input for a conditional check, triggering a follow-up flag only for negative feedback. You can also wrap results in TRIM() to clean whitespace, or use VALUE() when the AI returns a number as text so you can use it in calculations.

Another useful pattern is combining ARRAYFORMULA with AI for batch headers:

=AI("List the top 3 themes from these responses as a comma-separated list", A2:A50)

By passing a range rather than a single cell, you can get aggregate analysis across multiple rows in one call.

If you work with messy datasets regularly and want to skip the formula setup entirely, tools like VSLZ AI let you upload a file and ask questions in plain English to get analysis, charts, and summaries from a single prompt.

When to Use It and When to Reach for Something Else

The =AI() function is best suited for text processing tasks on small to medium datasets within Google Sheets. It handles categorization, extraction, sentiment scoring, and summarization well when your data fits within a few hundred rows.

For heavy statistical analysis, datasets with tens of thousands of rows, or real-time dashboards, dedicated analytics platforms or database connections will perform better. The AI function is not a replacement for a data pipeline, but it is a powerful addition to your spreadsheet toolkit that eliminates repetitive manual work on text-heavy data.

As Google continues expanding Gemini capabilities across Workspace, expect the function's capacity and speed to improve over time. For now, start with the use cases above and build from there.