Applications of AI in a Spreadsheet

April 20, 2025

Spreadsheets have long been the workhorse of data organization, calculation, and analysis. From simple budgets to complex financial models, their grid-based structure provides a powerful way to manage information. However, dealing with unstructured text, complex data cleaning, pattern recognition beyond simple formulas, and generating insights often required manual effort or specialized tools outside the spreadsheet environment.

The integration of Artificial Intelligence (AI) directly into spreadsheet functions is changing this paradigm. Now, users can leverage the power of large language models (LLMs) and other AI techniques to perform tasks that were previously difficult or impossible within the confines of rows and columns. This document explores the diverse applications of AI in spreadsheets, transforming them from static data containers into dynamic tools for insight generation, automation, and enhanced productivity.

We’ll explore capabilities typically accessed through functions like AI_ASK, AI_TABLE, AI_LIST, AI_EXTRACT, AI_FORMAT, AI_FILL, and AI_CHOICE, focusing on the what and why – the practical applications across various domains.

Core AI Capabilities in Spreadsheets

Modern AI spreadsheet functions bring several core capabilities directly to your data:

1. Natural Language Understanding & Generation: Interacting with your data using plain English, generating text, answering questions, and summarizing content (AI_ASK).
2. Data Extraction & Structuring: Pulling specific pieces of information from unstructured text and organizing it into tables (AI_EXTRACT, AI_TABLE).
3. Data Cleaning & Formatting: Standardizing inconsistent data formats (dates, addresses, names) automatically (AI_FORMAT).
4. Pattern Recognition & Completion: Identifying complex patterns in data and filling in missing values or extending series based on examples (AI_FILL).
5. Classification & Categorization: Assigning labels or categories to data based on criteria or examples (e.g., sentiment analysis, topic modeling) (AI_CHOICE).
6. List Generation: Create lists based on prompts or existing data (AI_LIST).

Available AI Text Functions

The following functions leverage AI to work with text data:

Let’s delve into practical applications of these capabilities.

Application Examples

Market Research & Customer Feedback Analysis

AI functions can revolutionize how businesses analyze customer feedback, reviews, and survey responses.

Sentiment Analysis: Quickly gauge customer feelings from text.

• Input Data:

• AI Application (AI_CHOICE): =AI_CHOICE(B2, "Positive, Negative, Neutral")
• Output:

Topic Extraction: Identify key themes mentioned in feedback.

• Input Data: (Same as above)
• AI Application (AI_EXTRACT): =AI_EXTRACT(B2, "the main topic")
• Output:

Summarization: Condense long reviews or comments.

• Input Data:

• AI Application (AI_ASK): =AI_ASK("Summarize the following review in one sentence:", B2)
• Output:

Sales & Marketing Automation

Streamline lead management, content creation, and outreach personalization.

Lead Qualification: Categorize leads based on descriptions or interactions.

• Input Data:

• AI Application (AI_CHOICE): =AI_CHOICE(B2, "Hot, Warm, Cold")
• Output:

Extracting Contact Information: Pull emails, phone numbers, or company names from text blocks.

• Input Data:

• AI Application (AI_EXTRACT): =AI_EXTRACT(A2, "the email address")
• Output:

Data Cleaning and Standardization

AI can tackle messy data that traditional formulas struggle with.

Formatting Addresses: Standardize inconsistent address formats.

• Input Data:

• AI Application (AI_FORMAT): =AI_FORMAT(A2, "Standard US Address format: Street, City, ST ZIP")
• Output:

Cleaning Names: Correct capitalization and potentially identify parts of names.

• Input Data:

• AI Application (AI_FORMAT): =AI_FORMAT(A2, "Proper Name Capitalization")
• Output:

Content Creation & Brainstorming

Leverage AI to generate ideas and draft content directly within your spreadsheet planning environment.

Generating Ideas: Brainstorm blog post titles, marketing slogans, or feature names.

• Input Data:

• AI Application (AI_LIST): =AI_LIST("Generate 5 blog post titles about "&A2)
• Output (Example for Row 1):

Creating Structured Data from Text: Turn meeting notes or descriptions into tables.

• Input Data:

• AI Application (AI_TABLE): =AI_TABLE(A2, "Action Item, Owner, Due Date")
• Output:

Pattern Recognition and Data Augmentation

AI_FILL allows you to go beyond simple series filling (like 1, 2, 3…) to recognize complex relationships and fill data based on examples.

• Input Data & Goal: Fill in the ‘Category’ based on the ‘Product SKU’ pattern.

• AI Application (AI_FILL): Using example_range of the first 3 rows and fill_range of the remaining 3 rows with missing categories
• Output:

• Industry Inference Example: Use AI_FILL to infer the likely industry of a company based on its name and a few examples (e.g., provide examples like “Tech Innovations Inc.” -> “Technology”, “Global Logistics Co.” -> “Logistics”).

Combining AI Functions for Complex Workflows

The true power emerges when combining these functions. Imagine analyzing product feedback:

1. Paste Raw Feedback: Place raw customer feedback text into Column A.
2. Extract Product Name (AI_EXTRACT): In Column B, use =AI_EXTRACT(A2, "the product name mentioned").
3. Analyze Sentiment (AI_CHOICE): In Column C, use =AI_CHOICE(A2, "Positive, Negative, Neutral").
4. Extract Key Topics (AI_LIST): In Column D, use =AI_LIST(A2, "List key topics or features mentioned").
5. Summarize Feedback (AI_ASK): In Column E, use =AI_ASK("Summarize the feedback in one sentence:", A2).

This creates a structured dashboard from unstructured text, ready for pivoting, filtering, and further analysis – all within the spreadsheet.

Tips for Effective Use

• Be Specific with Prompts: The clearer your instruction (for AI_ASK, AI_FORMAT, AI_EXTRACT, etc.), the better the result. Instead of “fix this”, try “Format as YYYY-MM-DD date”.
• Provide Good Examples: For functions like AI_FILL or AI_CHOICE (when used with examples), ensure your examples accurately represent the desired output and cover different scenarios.
• Start Small: Test AI functions on a subset of your data first to understand their behavior and refine your prompts.
• Review Results: AI is powerful but not infallible. Always review the generated results for accuracy, especially for critical data.
• Understand Limitations: Be aware of potential biases in the AI models and any usage limits or costs associated with the functions.
• Consult Documentation: For detailed usage, parameters, and more examples, see the linked documentation for each function in the table above.

Conclusion

Integrating AI into spreadsheets transforms them from passive calculators into active partners in data analysis and workflow automation. By leveraging natural language processing, pattern recognition, and data structuring capabilities, users can:

• Quickly extract insights from unstructured text.
• Automate tedious data cleaning and formatting tasks.
• Generate creative content and ideas efficiently.
• Build more sophisticated and automated workflows.

As AI technology continues to evolve, its applications within the familiar spreadsheet environment will only grow, further empowering users to unlock the value hidden within their data. This shift marks a significant evolution, making advanced data manipulation and insight generation accessible to a much broader audience.