Microsoft has officially introduced it Co-pilot function In Excel for Windows and Mac, bring the power of large language models (LLM) directly into spreadsheets. This move represents a fundamental change: AI is now a local feature, not just an external add-on or a separate tool. Users can now use natural language prompts to analyze, summarize and generate data directly in their Excel formulas.
How the secondary function works
this Co-pilot function Allows the user to use natural language input prompts in any cell, referring cell values or ranges to context. The syntax is simple:
text=COPILOT(prompt_part1, [context1], [prompt_part2], [context2], ...)
- Prompt_part: Text describing AI tasks (e.g., “Classification of this feedback” or “Summary A1:A50”).
- context (Optional): References to the cell or range processed by AI.
Key Features
- Instant AI-driven analysis: Enter prompts and context – This feature generates a summary, category, or list that is updated instantly as the data changes.
- Seamless integration: Copilot is located in Excel’s computing engine and works with standard features such as if, Switch, or Lambda, allowing traditional Excel logic to merge and get AI-generated results.
- Dynamic update: No need to rerun the script or refresh the add-in. Any changes to the data will automatically recalculate the AI output.
- Multiple use cases: From sentiment analysis, summary generation, SEO keyword brainstorming to table/list creation, Copilot adapts to a variety of analytical and creative workflows.
Example usage
- Classified feedback: text
=COPILOT("Classify this feedback", D4:D18) - Generate list: text
=COPILOT("Generate airport codes for major US cities") - Summary of data: text
=COPILOT("Summarize", A1:A50)
Technical Insights
- Automatic update and confidentiality: Copilot automatically updates as data changes and does not use user-provided data to train the underlying AI model, ensuring confidentiality.
- Function call limits: Allow users 100 calls every 10 minutes and 300 per hour. Taking an array as a single call can help maximize usage efficiency.
- Integrate with existing features: Copilot results can be used as input to other formulas that greatly expand the analysis capabilities in spreadsheets.
- No external data access yet: Copilot uses only data from within the workbook; it plans to support real-time network data and internal business documents for future updates.
- Optional implementation: This feature has been opted in and is only present when you choose to use it.
Availability and launch
- Beta Channel Users: Available for Microsoft 365 Copilot license holders, running Windows Excel version 2509+ or Mac version 16.101+.
- Excel for Web: The program will be launched soon through Frontier.
- Comparison with Chatgpt integration: Unlike external AI chat tools, Copilot provides The country’sAutomatic Recovery Analysis – Workflow Gap for Excel users, who previously relied on replication processing between applications.
Use case situation
- Brainstorm: Quickly generate SEO keywords, rewrite messaging or conceive product features.
- Category and tagging: Classify support tickets, survey responses, or feedback by scale without external tagging tools.
- Reports and Summary: Turn complex data sets into digestive narratives for stakeholders.
- automation: With one prompt, fill the grid immediately, build lists, tables, and project plans.
Tips for using Copilot
- Timely engineering: Clarity is important – explicitly state, specify the range and set the desired output format (e.g., tables, lists).
- instruction: Use action-oriented instructions (“summary”, “categorize”) for better results.
- verify: Always look at the output generated by AI, especially for critical business tasks.
- Usage strategy: Maximize calls by referencing arrays with single cells.
in conclusion
The Copilot feature marks a critical moment for Excel, embedding advanced AI directly into the most widely used analytics platform in the business. With the ability to generate, analyze and summarize data, Excel is changing the way users interact with data – making workflows smarter and more efficient. Local integration bridges the gap between spreadsheets and AI and marks the future of generative models becoming the default companion in business analytics.
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Michal Sutter is a data science professional with a master’s degree in data science from the University of Padua. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels in transforming complex datasets into actionable insights.
