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Build Dashboards and Kanban Boards with the Google Sheets Canvas Tool

With the new Sheets Canvas feature, we can build professional-looking interactive dashboards directly in our Google Sheets, using nothing more than natural language prompts. It’s a huge upgrade from how we used to build dashboards in Sheets.

Sheets Canvas to build a professional dashboard

Previously, we clumped together charts and used formatting tricks such as borders and background cell colors to try and recreate a professional look. It was all rather challenging and time-consuming, with limited interactive options. (Read this post to see what I mean.)

Alternatively, we might have connected Google Sheets to a third-party tool, but these incurred extra costs, introduced security risks, and were often still pretty janky.

In this post, we are going to take a look at this new Sheets Canvas feature.

Sheets Canvas Availability

At the time of writing (August 2026), the Sheets canvas feature is only available to Google Workspace customers on Business or Enterprise Standard and Plus plans, or AI Pro for Education add-on subscribers, or to consumers on Google AI Pro and Ultra plans.

How To Build A Dashboard with Sheets Canvas

Firstly, you need to have your data ready to go in a Google Sheet, preferably in a Tables format, which is best for Gemini.

Secondly, it pays to think about your dashboard design before you start building it.

That way, you won’t end up building something that isn’t useful. Instead, you plan what information will be shown on the dashboard and how it will work, which will result in a more successful outcome.

For example, ask yourself these questions:

What are the key metrics to show?
Who is the intended audience for this dashboard?
How technically literate are they? Will they understand how to read complex charts?
How familiar are they with this data and its background context?

With your data and design plan to hand, you’re ready to start creating a Sheets Canvas.

There are three entry points:

From the toolbar: Insert > Canvas
Using the “Create a canvas” button in the bottom left of your Sheet, next to the Sheet names.
(Both 1 and 2 open the Gemini sidebar with the Canvas option pre-selected.)
You can select “Create canvas” directly in the Gemini sidebar too.

1. Insert menu:

2. Create a canvas button:

Insert button for Canvas feature in Google Sheets

3. Create canvas in Gemini Sidebar:

Canvas feature in Gemini sidebar of Google Sheet

With Gemini sidebar open and the canvas feature selected, make sure you select the correct dataset(s) for the dashboard.

Use the button next to the Canvas option and choose the sheet containing your data. If you already have that sheet open, then it will automatically be selected, and you can deselect the other sheets if you don’t need them.

Now you’re ready to insert your prompt.

Example 1: Real Estate Dashboard

Consider this dataset of real estate transactions:

Here’s an example prompt to build a dashboard for this data:

Build a comprehensive real estate transaction dashboard to track performance, commissions, and lead generation. Include the following elements:

1. Key Performance Indicators (KPIs): Calculate the total sum of Sales Price, Commission Income, Agent Commission, and Net Profit for Tax for all “Closed” and “Paid” deals.

2. Agent Performance: Create a summary table or chart showing the total Commission Income and Net Profit generated by each Agent.

3. Lead Source Analysis: Create a bar chart breaking down total Sales Price and total number of transactions by Lead Source to see which marketing channels are most effective.

4. Buyer vs. Seller Split: Create a visual breakdown comparing the number of transactions and total Commission Income based on the Deal Side (Buyer vs. Seller).

5. Deal Pipeline: Provide a quick count of transactions categorized by Deal Status to show how many deals are currently pending versus closed.

Please format this with a clean, professional layout, using bold headers for each section and placing the charts logically next to the summary tables.

Use hex color #0F9E59 as the primary color and #9E0F9C as the secondary color.

Pro Tip: Save your best dashboard prompts in a prompt library for future reuse.

Typically, Gemini takes a few minutes to build the dashboard, depending on how complex your prompt is.

The output for the real estate data example looks like this:

A real estate dashboard built using Google Sheets Canvas

Notice how the colors match the colors mentioned in the prompt (#0F9E59 as the primary color and #9E0F9C as the secondary color).

Also, notice the “Add New Deal” button in the top right corner. When you click this, it opens a pop-up with a form to add new deals:

This data is inserted directly into your original data table in Google Sheets. It’s a great way for users to add new data without leaving the dashboard view, and it saves users from needing to navigate the data in the spreadsheet itself.

At the the top of the canvas is a small edit bar for making changes, sharing, or deleting the canvas:

To use the “Copy link” button to share the canvas, you need to first grant file access (through the regular “Share” button in the top right of your Google Sheet) to anyone you want to share the canvas with.

Example 2: Kanban Board

The Sheets canvas tool is not limited to regular dashboards, though.

It can also build interactive Kanban boards, which is my favorite use case and a killer feature.

Build a Kanban board in Google Sheets with the Canvas feature.

Kanban boards are visual project management tools that organize tasks into columns and allow you to drag and drop cards between these columns.

Previously, this sort of interactivity was impossible inside of Google Sheets, but now with this new Sheets Canvas feature, you can build a fully featured Kanban board with a single prompt.

For example, here is a client pipeline data set for training contracts:

And here’s an example prompt to generate a Kanban board from this data:

Please generate a Kanban board layout with the following specifications:

1. Structure: Group the data using the “Stage” column.

2. Card Details: Under each Stage header, create individual “cards” or grouped cells for each transaction. Each card should display the most critical information at a glance:

Company & Client Name

Deal Size & Expected Value

3. Formatting:

Please apply conditional formatting or color-coding to the cards based on the “Probability” column (e.g., green for high probability, yellow for medium, red/gray for low) so I can easily identify which deals are most likely to close.

Ensure the layout is clean and minimalist.

The output from this prompt looks something like this:

You can then drag these cards into different columns as needed to show progress. When you drag a card into a new column, the data in the underlying sheet also updates to reflect that, so they are always synced in real time.

And as with the dashboard from earlier, there is also a button to add new data from the Kanban canvas rather than having to go back to the actual spreadsheet data.

Limitations

At the moment, if you want to modify the dashboard generated by the Sheets Canvas tool, you give it an additional prompt in the Gemini sidebar. For example, you might prompt Gemini with “change the leads pie chart into a bar chart“.

Unfortunately, when Gemini updates the dashboard, other elements might inadvertently change too.

I think the real breakthrough will come when we can click on individual features within the Dashboard or Kanban board and give specific instructions to modify that specific feature only with a prompt. Hopefully that is something coming down the line.

Conclusion

This new Sheets Canvas feature is a fantastic tool that dramatically expands the possibilities of what you can do with data in Google Sheets.

And, whilst the regular chart tool will remain extremely useful for specific analysis and one-off charts, the old way of building dashboards is rapidly becoming obsolete.

I can’t imagine spending hours manually positioning charts, hand-picking background cell colors and borders, and all manner of other dark arts, to build a dashboard the old way, knowing it wouldn’t even look half as good as what this new canvas tool can build in a few minutes.

If you have access to this canvas tool, then I encourage you to experiment with it to build your dashboards.

Is Formula Writing Dead? (And why we shouldn’t mourn it)

Why spend hours crafting formulas yourself if Gemini can do it for you?

For the better part of two decades, I’ve viewed my spreadsheet work through the lens of a craftsperson, rather than as a corporate analyst.

The grid of cells was my canvas and the formula my brush.

I pushed the limits of what formulas could do, creating clocks, chess games, etch-a-sketch, and join-the-dots games, using only built-in formula logic.

But recently, over the past few months, something has shifted.

We’re seeing the “formula syntax barrier” dissolve in real-time. Years of experience and a deep knowledge of the function landscape is no longer required to write complex formulas.

The true impact of this realization hit me while I was experimenting with a new formula game: building a Minesweeper demo in Google Sheets, using only formulas. (Spoiler: I don’t believe it’s possible to do fully, without using code to control the game play.)

I started the traditional way, experimenting with various OFFSET functions to “see” what was in the adjacent cell to the formula. It was intellectually satisfying, but also slow going.

The New Way

Once I had a working OFFSET formula that checked a single cell, I decided to see what Gemini would do. Truthfully, I couldn’t justify the time doing it myself when I had “real” work to do (or, was I just too lazy to do that much hard thinking?).

So I plugged the formula into Gemini with this prompt:

I have this formula in Google Sheets that checks whether the cell directly below is equal to "A". It gives an output 1 if the cell below equals A and blank otherwise:

=IF(OFFSET( INDIRECT( ADDRESS( ROW(), COLUMN())),1,0)="A",1,)

can you extend this concept and create a formula that checks if any of the adjacent cells that border the formula cell are equal to "A". There are 8 cells that surround the formula cell.

The formula Gemini generated was complex:

=IF(COUNTIF( OFFSET( INDIRECT( ADDRESS( ROW(), COLUMN())),-1,-1,3,3),"A")-IF( INDIRECT( ADDRESS( ROW(), COLUMN()))="A",1,0)>0,1,"")

But guess what? It worked. It just plain worked!

It extended the logic to check if there was an “A” in any of the cells surrounding the formula cell.

I like to think I would have got there myself, but it would have taken me a LOT longer than the minute that Gemini took.

After more back-and-forth with Gemini, where I only directed Gemini and didn’t write any more formulas myself, I had a working demo of Minesweeper:

It’s running entirely from a single formula in cell B8, the top left of the grid.

Feel free to grab a copy of the template here, to explore the formula.

Now, it’s not playable in the traditional sense (that would require some coding) but it shows sort of how Minesweeper works.

What used to take me hours of deep-focus was delivered in minutes. And instead of getting stuck on syntax, I was free to focus on the high-level problem and design.

And here’s the thing. I could have just as well started with a text only prompt and skipped creating that first formula, and Gemini (or another AI tool) would have gotten the answer for me.

The Economic Argument

When time is money, writing complex formulas manually does not make economic sense. If it takes you an hour to figure out a complex array formula but only 30 seconds for an AI tool to generate it, then the “hand-crafted” version is no longer a demonstration of skill but instead a waste of resources.

Writing formulas today feels increasingly like hand-washing laundry when you own a functional washing machine.

Changing Nature of Work

Our role is shifting (has shifted?) from creator to director.

We now iterate our way to a solution through a conversation with an AI tool, refining logic and debugging via prompts.

However…

Being “formula literate” remains a superpower.

My Minesweeper experiment succeeded because of my years of honing my craft. I knew that the OFFSET function was key to making it work. And then I knew how to write the best prompts to get the solution I wanted.

So although you don’t necessarily need to master every function, parentheses, or comma anymore, it helps enormously to be able to audit the logic and spot mistakes.

I’d also make the case that for simpler formulas (e.g. a simple XLOOKUP to join data tables), it’s quicker to write the formula yourself, rather than switching to an AI tool.

The Verdict

The goal was never to write the perfect formula; the goal was always to solve the problem.

If we reach the answer ten times faster by delegating formula syntax to Gemini, we haven’t lost our craft or diminished our work. Rather, we’re being smart with our time and opening ourselves up to solving more advanced problems.

So don’t mourn the manual formula.

Instead, use the hours you claw back to tackle the problems that AI can’t solve: business strategy and the human impact of the results on your customers.

And a Closing Caveat

Yes, AI is very good at generating formulas and fixing formula syntax.

But YOU are still much better at building entire spreadsheets, managing data, communicating with your clients, and solving complex problems.

So don’t get too drawn into the AI hype. By all means, work faster by leveraging AI tools but lean into your human experience to solve the bigger problem.

Now go forth and conquer those complex formulas! (With a little help from Gemini.)

How to Build a Content Engine with Apps Script, NotebookLM, and a Gemini Gem

Most of us are sitting on an absolute goldmine of unstructured data.

Think about it: years of client reports, research notes, project documentation, YouTube videos or, in my case, a decade’s worth of newsletter archives.

Usually, this information just sits there dormant. Why? Because it’s spread all over the place, too tedious to search through and catalog, and way too dense to summarize manually.

But by combining Apps Script, NotebookLM, and Gemini Gems, we can transform that forgotten archive into an AI content engine. We’re talking about a system that can access all your content and generate new work based on that specific foundation.

Why NotebookLM AND a custom Gem?

By using NotebookLM as the data foundation and a Gemini Gem as the creative interface, we create a workflow that captures the best of both worlds.

NotebookLM acts as a secure vault containing our data, ensuring that new derivative work is strictly grounded in our own history. However, NotebookLM is a research tool built more for consuming (reading, listening, slide decks, flash cards etc.) rather than creating “new” content. It lacks the ability to remember our voice between chats or access the live web.

A Gemini Gem, on the other hand, serves as a creative foil. It can remember our specific voice and formatting preferences, so we don’t have to explain this each time. It can also access the web to incorporate external data, if required. And it can generate images and video too.

This architecture allows us to take our archived knowledge and quickly transform it into new, high-quality content.

How To Build a Content Engine

To build this workflow, we need to solve for three things: 1) extracting our data, 2) storing our data, and 3) generating new content from this data.

If you don’t have much source data then you can just add it directly as sources in your NotebookLM (go straight to step 3 below).

But if you have a lot of source data (in my case, 375 newsletters at about 500,000 words and 240 blog posts at about 700,000 words) then it’s not as simple as just uploading them one-by-one to NotebookLM. For one thing, you can’t upload that many separate source docs to NotebookLM (the limit on the free plan is 50 sources). Secondly, can you imagine how tedious it is to manage almost 600 different sources!

The solution is to create a script to scrape all these web links and dump the content into a series of Google Docs. I created ten Google Docs, each with about 60 pieces of content. Don’t try to put everything into one single giant Doc, as again, you might run into the size limits in NotebookLM, which is up to 500,000 words per source.

1. Code Your Scraper (Optional)

I started with a Google Sheet containing a list of newsletter subject lines in column A and the URLs of the hosted newsletter content in column B, with 375 rows in total.

Don’t worry, we don’t need to write the script ourselves from scratch! Simply prompt Gemini to write a Google Apps Script for you.

I used this prompt in Gemini to generate the script that would visit each URL in turn and save the text into a Google Doc. To ensure it could handle the volume of data without timing out (Apps Script has a 6-minute limit) I asked Gemini to grab content in batches and keep track of rows, so the script could “resume” where it left off.

Generate a Google Apps Script that takes a list of website links from a sheet called ‘Newsletter URLs’ and saves them into a single Google Doc. Put the subject line from Column A as a Heading 1 and then pull all the text from the URL in Column B underneath it.

It needs to remember where it left off if it hits a time limit, so I can just hit ‘Run’ again to pick up the progress. Also, make sure it pauses for a few seconds between each link and refreshes the document connection every few rows so it doesn’t crash or get blocked by the websites.

In your Sheet with the list of links, go to Extensions > Apps Script

Copy in the script that Gemini created.

Make sure your insert the correct ID from your Google Doc

To begin with, run a test with 5 URLs to ensure the script works as intended. If you encounter any errors, paste them back into Gemini and ask for a fix.

Once you’re happy its working, move to step 2 and start consolidating your online content into Google Docs.

2. Extract and Consolidate Your Data

Run your script from the Google Sheet containing your source URLs. It will visit each link in turn and paste the data into your Google Doc in batches.

This centralizes disparate web data into a single, clean Google Doc.

3. Create Your NotebookLM Archive

Visit NotebookLM and create a new notebook.

Upload your consolidated Google Docs as Sources.

This “grounds” the AI, ensuring it only uses your verified history and doesn’t “hallucinate” outside facts.

4. Distill Your Voice to Create a Style Guide

Back in Gemini, in a new chat, connect it to one of these Google Docs, which contains your best writing.

Prompt Gemini to analyze your voice and identify your unique formatting habits, vocabulary preferences, and “persona”.

Based on the attached google doc of my writing published online, write a style guide for a custom Gem that captures my voice. The instructions should enable the gem to write in my voice, using a notebooklm backend with access to all my previous published writings

In my example, this was the response from Gemini, which was a good starting point that could be tweaked to create my unique style guide:

Once you’re happy with the response, save it as a Style Guide in a Google Doc.

5. Build the Gem (The Interface)

In Gemini, open the Gem Manager and create a New Gem.

Link your NotebookLM archive in the “Knowledge” section and paste your Style Guide rules from section 4 into the Instructions box.

Hit Save and move to step 6!

6. Use Your Gem to Create New Content

Test the Gem with a prompt like: “Using the logic from [Source Name], draft a new response in my voice”.

In my example, I tried this prompt:

draft a newsletter about the VSTACK function

And here is the newsletter Gem taking this simple start prompt about the VSTACK function and generating a newsletter that is a pretty good mimic of the real thing.

Of course, if I was to use this example, I would need to review the text with a fine tooth comb, check all the examples, fix the links, and rewrite sections to make it my own.

However, as a first step, to generate ideas or scaffolding, it’s incredibly helpful.

Now, instead of spending hours drafting, I can ask my Gem to “draft a new tip about data validation based on the logic I used in Tip #210.” It generates a plausible draft in my voice, using my historical data, in seconds.

How to use the AI Function in Google Sheets (with 10 examples)

The AI Function in Google Sheets brings the power of Gemini AI directly into your cells.

It uses Gemini’s large language models (LLMs) to process the text prompts and return helpful responses. It’s akin to using a prompt in the chat window of Gemini, but with the convenience of being accessible in your Sheet.

And it’s as easy to use as any other formula!

Who has access to the AI Function?

The AI Function is available to people on the following Google Workspace plans:

Business Standard and Plus
Enterprise Standard and Plus
Customers with the Gemini Education or Gemini Education Premium add-on
Google AI Pro and Ultra

Anyone who previously purchased these legacy add-ons will also have access to the AI function:

Gemini Business
Gemini Enterprise

Additionally, it’s available to users enrolled in the Google Workspace Labs program.

☠️ Note: If you don’t have access to the function, you can achieve any of these results by copy-pasting data into Gemini (or ChatGPT or Claude) and running the prompt there. And then you can paste the results back into your Sheet. Obviously, it’s not as convenient!

How to use the AI Function

The AI function is simple to use and has the following form:

=AI( "prompt" , [optional range] )

It takes two arguments:

the prompt, which is a description of the action you want the function to perform, enclosed in double quotes, and
an optional range, which is a reference to a single cell or range containing information that provides extra context to the AI function.

The AI function is most useful for working with text. There are four broad actions that it can perform:

Sentiment analysis
Summarizing text
Categorizing text
Generating text

Example 1: Sentiment Analysis with the AI Function

AI function to perform sentiment analysis in Google Sheets

Suppose you manage an AirBnb and you have a Google Sheet containing reviews. You can use the AI function to analyze the sentiment of these reviews to identify positive and negative reviews.

Use this prompt:

Categorize sentiment as positive, negative, or neutral

inside the AI function as follows:

=AI( "Categorize sentiment as positive, negative, or neutral" , D6 )

where the cell D6 contains the review.

Alternatively, we can include the reference to cell D6 directly in the prompt argument by concatenation:

=AI( "Categorize sentiment of the review in " & D6 & "as positive, negative, or neutral" )

In this case, we don’t specify the option range argument.

Perhaps the only benefit of this second approach is that it lets you reference non-contiguous ranges, i.e. multiple cells in different parts of your Sheet.

For example, this text generation AI formula references cells in column B and D (i.e. non-adjacent), which wouldn’t be possible with a single range argument:

Using cell references inside the AI function

But if you are using a single cell or range, it’s easier to include it as the second argument of the AI function.

Example 2: Sentiment Analysis as a Rating Chip

An alternative way to show sentiment analysis is to ask the AI function to output a rating between 1 and 5 and then format the cells as Rating chips.

The AI formula is:

=AI( "Rate the feedback in this review between 1 and 5, with 1 being the worst score and 5 being the best score" , D6 )

This gives an output between 1 and 5 (technically, 0 to 5 would also work).

Then highlight the cells with the rating numbers and change them to Rating chips via the menu:

Insert > Smart chips > Rating

Alternatively, if your data is in a Table, you can change the column type to a Rating chip under the column menu:

Edit column type > Smart chips > Rating

Example 3: Summarize Text

With the review text in cell D6, we can use this formula to summarize the text:

=AI( "write a summary of the user's feedback in under 10 words" , D6 )

Example 4: Categorize Text

Suppose we have a list of products that we need to categorize into groups. It’s a hugely time consuming task.

The AI function can dramatically speed that process up.

With the data in column B, we can use a formula like this to categorize the products:

=AI( "categorize the product into one of these consumer electronics categories: Audio,Home Entertainment,Mobile Device,Wearable,Home Appliance,Gaming,Smart Home,Computing,Photography,Kitchen Appliance,Drones,Mobile Accessory" , B7 )

Example 5: Generate Text

Generate marketing copy in Google Sheets with AI formulas

In this example, we use the AI formula to generate marketing copy for products on Instagram. We tell Gemini to add emojis and hashtags in the output.

Note also that because the product name is in column B and the key features are in column D, we need to insert the references into the prompt:

=AI( "Generate a short less than 20-word description for instagram for this product " & B6 & " with these features " & D6 & ". Make it funny, and include emojis and add relevant hashtags" )

This just inserts the value from cells B6 and D6 into the text string prompt before the AI formula runs it.

Example 6: Generate Email Drafts

We use a longer, ultra-specific prompt to ensure the emails are formatted exactly as we want them.

And the formula references data in the range B6 to G6 (i.e. the whole Table row) for context:

=AI( "Write a professional email to the parent or guardian of this student, summarizing their academic performance and classroom behavior.
The email should be written from me, Ben Collins. I am their teacher.
Start each email with a subject line formatted exactly as:
Subject: Name of Student’s Progress Repor
For example: Subject: John Smith’s Progress Report.
The tone should be warm, supportive, and informative—highlight both strengths and areas for improvement.
Avoid overly technical language, and keep the email clear and easy to understand.",B6:G6)

This is part of a YouTube tutorial on turning Google Sheets data into Gmail drafts, with the AI Function and Apps Script.

Example 7: Explaining Formulas

At the time of writing (August 2025), the AI function cannot be nested inside other functions, or vice versa.

To combine it with other functions, we have to create helper columns that output intermediate steps.

So before we can explain that complex formula in cell A1, we have to use the FORMULATEXT function to turn it into text:

The formula is:

=FORMULATEXT(A1)

Then we can use the AI function to explain that text version of the formula:

Explain formulas with the AI function in Google Sheets

The AI formula is:

=AI("explain what this formula does and how it works",C1)

Example 8: Categorize Stock Tickers

We can use the AI function to categorize stock tickers into industry categories:

Categorize stock tickers with the AI function in Google Sheets

The formula is:

=AI("show me the sector of the stock with this ticker", stockTickers[Ticker])

where the stock tickers are in a Table called “stockTickers”.

Example 9: Parse Addresses to Extract Data

The AI function is a huge help at parsing messy addresses and extracting the component parts:

Parse addresses with the AI function in Google Sheets

The formulas for these columns are:

=AI("show me the street address only from this address",A2)

=AI("show me the town only from this address",A2)

=AI("show me the postcode only from this address",A2)

Example 10: Unpivot Data

For a final example, here it is helping to UNPIVOT data, which is turning wide data into tall data format. It’s notoriously difficult to do with regular formulas.

However, if you can handle complex formulas, the old-school, deterministic method is still more convenient since we can’t nest the AI function inside other functions yet.

But for reference, here’s how we can create an AI formula to unpivot data.

First, we unpivot data with AI:

Unpivot data with the AI function in Google Sheets

The formula is:

=AI("can you unpivot the data",A1:E4)

Then we use regular functions to parse into rows:

The formula is:

=ArrayFormula(SPLIT(TRANSPOSE(SPLIT(G1,CHAR(10))),"|"))

What the AI Function CANNOT do

1) it cannot perform calculations like regular functions. If you try, you’ll get an error message:

With all the hype around AI at the moment, it’s tempting to apply it to everything we do.

So it’s good to step back and ask yourself if you really need an AI function. Most spreadsheets tasks that involve data will still be in the domain of regular, deterministic functions, like SUM, AVERAGE, FILTER, etc.

So don’t fall into the trap of trying to apply AI to everything!

2) it cannot be nested inside other functions.

If you try to nest the AI function, for example inside an IF function as shown here, you’ll get an error message:

3) the AI function has limits to how much it can generate in a 24-hour window. If you hit the limit, you won’t be able to click the “Generate” button until 24 hours has passed.

4) if you select multiple cells with AI functions and click to generate outputs, only the first 200 cells will be generated. Once the first batch of 200 is complete, you can generate the next batch of 200 cells, and onwards.

5) if you access Sheets through other cloud storage providers (e.g. Dropbox, Box, etc.) then you can’t generate content with the AI function.

6) The AI function cannot generate other functions, pivot tables, or charts.

AI Function Template

Download the Complete Guide to the AI Function Template

Click on “Use Template” in the top right corner to make your own copy.

There is no Apps Script with this template.

Additional prompt examples in the Google documentation.

I Let OpenAI Operator Manage My Google Sheet: Here’s What Happened.

OpenAI recently launched their agent tool called Operator. It’s an AI powered tool that can navigate the web and perform tasks for you using its own browser. It can click, type, scroll, and generally interact with web pages the way you or I would.

Naturally, I tested it out with a Google Sheets project to see how it fared. Specifically, I asked it to complete 3 tasks for me:

Create a pivot table
Create a chart
Write a script to email a pdf copy of the Sheet to me

Here’s how it got on:

OpenAI Operator in action

I pasted the URL of my Google Sheet into the Operator chat window and asked it to get to work.

It opened the Sheet in a Linux browser within Operator and then handed control to me to login to the Sheet. Once that was complete I handed control back to Operator to work it’s magic.

It was wild watching the computer take control of my Sheet. I watched it open menus, insert objects, and write code.

The craziest moment was when it made a mistake with the pivot table and self-corrected 🤯.

Well, that, or when it opened my Apps Script editor and started coding!

But it wasn’t all plain sailing.

It got itself completely stuck when it was trying to save the script it wrote. It kept pressing the “Undo” button and, eventually, I had to step in and tell it where the “Save” button was.

I felt like I was like working with a digital assistant.

I’m excited for this to go fully multimodal. Imagine working through this scenario with voice activation instead of using a chatbot.

We overestimate the short-term change, but underestimate the long-term change. It feels like we’re moving pretty darn fast at the moment 😯.

Operator Availability

At the time of writing (27th January 2025) Operator is available to OpenAI Pro users in the U.S..