Unfortunately, I have missed one return deadline too many. So I built a ChatGPT return tracker. A Scheduled Task scans my inbox for online purchases, flags upcoming return deadlines and sends reminders to my phone. When I decide to send something back, a reusable Start Return skill finds the order, opens the retailer’s return portal and retrieves the QR code or shipping label. This is a quick, real-life example of the kind of Scheduled Task I covered in How to Automate Tasks with ChatGPT or Claude: A Beginner’s Guide.

How my ChatGPT return tracker works

The “Return Monitor” task scans my inbox every morning for new online purchases and relevant order updates. I have it look for order confirmations, shipping notices, delivery emails, and return confirmations. If a retailer starts its return window on the delivery date rather than the purchase date, the delivery email helps it calculate the correct deadline.

I have also configured it to:

• Exclude retailers that I would rarely make returns to including Amazon, Target, grocery stores, etc. I can always refine these filters later if my behavior changes.

• Track the retailer, order number, item, delivery date and return deadline.

• Distinguish between a deadline stated directly in an email and one inferred from a retailer’s published return policy on their website.

• Flag anything ambiguous for my review, such as final sale merchandise or special holiday return windows, rather than guessing.

• Remind me 7 days and 2 days before the return window closes.

• Stop tracking an item as soon as I tell it I’m keeping it.

No spreadsheets. No starred emails. No to-do list. My system finds the purchases and surfaces the decision to me at the moment I actually need to make it.

The most important reminder happens after I start the return

Starting a return and completing it are, unfortunately, two different tasks. Especially those of us who live in NYC and must haul packages to the UPS store on foot. I unfortunately know from experience that I’m capable of requesting a return, generating the QR code, taping the box shut and still leaving it sit in my apartment collecting dust, saying I’ll “return it tomorrow” until the drop-off deadline passes (no judgment please).

This tracker does not consider the job finished once the label exists. It continues reminding me of the drop-off deadline (which is sometimes something like seven days after the QR code was generated) until I confirm I actually dropped it off.

This was the task that finally convinced me to ChatGPT notifications on my phone. A drop-off reminder is only useful if I see it before I leave the house, not the next time I open my computer.

I added a “Start Return” ChatGPT Skill to finish the job

My Scheduled Task itself handles the memory, monitoring, and timing. A custom skill handles the return request itself.

Skills are reusable workflows that teach ChatGPT or Claude how to complete a particular job. You can invoke the skill at any time in a chat. They can include step-by-step instructions, preferences, decision rules, and conditions for when the AI should stop and ask for clarification.

Scheduled tasks are useful for work that should happen repeatedly or at a particular time. Skills are more on-demand. The benefit is, instead of explaining the same process to ChatGPT every time, I can invoke a workflow I have already taught it.

In this case, when my tracker reminds me that a return deadline is approaching, I can respond:

Start Return for the navy dress.

The Start Return skill then:

1. Finds the right order confirmation, shipping, or delivery email.

2. Confirms which item I meant if it cannot confidently identify it within an order containing multiple products.

3. Opens the retailer’s official return portal.

4. Works through the return process.

5. Applies my saved preferences.

6. Retrieves the final QR code or shipping label (and in the future, will send it to my printer!)

If it encounters a choice it cannot safely make from my existing preferences, it will pause and ask for my clarification and approval.

I preconfigured my return preferences

In teaching the system how I usually make return decisions, I can make the process more autonomous rather than asking me a bunch of follow-up questions. I saved the following preferences and instructions:

1. My preferred return methods are: Happy Returns > QR code that I can bring to a drop-off location > Printed shipping label.

2. My preferred refund method is always original payment method instead of store credit (and if original payment method isn’t available, the AI should pause and ask me what to do next).

3. Tell me when returning something in person could avoid a shipping label fee.

4. Choose a return reason related to “fit” of the item. If that’s not available, ask me how to proceed.

5. Pause and ask me for clarification if my preferred options are not available.

These instructions are what actually save me time in the Start Return process. If I had to answer every question for the AI, I might as well have completed the return form myself.

The first attempt wasn’t perfect

The first time I used Start Return, it missed one of the available return methods. It told me Happy Returns wasn’t available and that my only option was a UPS shipping label I would need to print. I could see Happy Returns selected as the default option on my screen, so I sent ChatGPT a screenshot and asked it to try again. The second attempt worked, and it completed the return correctly. Within minutes, I had a Happy Returns QR code in my inbox.

Afterward, I asked ChatGPT to update the skill based on what had gone wrong. It summarized its proposed changes and waited for my approval. When I forgot to approve the changes, it even sent me a push notification the next morning saying the task needed my attention.

That small repair loop of showing it what it missed, asking it to retry, and then folding the lesson back into its instructions, is what got the skill back on track and will make it more durable in the future.

How does ChatGPT click through an online return portal?

ChatGPT and Claude can use a built-in browser to open web pages, click buttons, enter information, inspect what is on the screen, and move through a website.

How can it do this? Computer Use.

Computer Use allows ChatGPT and Claude to see and operate interfaces like web browsers and desktop apps that don’t have a direct integration.

A good way to think about these capabilities is:

• Skills are playbooks

• Computer Use provides the hands

• Scheduled Tasks provides the clock

I don’t own a printer, but I’m planning on getting one to test a final step in this skill that triggers the label printing.

Why ChatGPT cannot complete every online return

Be aware that AI will likely not be able to handle every return. Unfortunately, the world of internet browsers has not fully caught up to AI surfing the web. For years, websites have been built to explicitly block bot traffic. So, sometimes it gets suspicious of AI computer usage as being a bot. Retailer websites may present CAPTCHA challenges, two-factor authentication, email verification links, or other bot detection systems.

My built-in rules for this skill direct the AI to pause and ask me for clarification if it encounters challenges successfully navigating the retailer’s website.

A quick privacy PSA

As AI becomes more connected to our inboxes, files, browsers, and other personal systems, it’s worth spending a few seconds reviewing your data settings in your AI provider.

I personally keep model training turned off on my consumer AI accounts.

For ChatGPT:

Profile → Settings → Data Controls → turn off “Improve the model for everyone.”

OpenAI says new conversations will remain in your chat history but will not be used to train its models after this setting is disabled. You can review the current instructions in OpenAI’s Data Controls FAQ.

For Claude:

Name → Settings → Privacy → turn off “Help Improve Claude.”

Anthropic’s current instructions are available in its Model Improvement privacy settings guide.

Turning off model training is not the same thing as preventing the service from processing or retaining any data required to complete your request. I also recommend reviewing which apps and accounts you have connected and giving each AI system only the access it actually needs.

What this system gives me

Now instead of relying on myself to remember which packages need to go back and when each deadline is, I have a system that keeps track of it for me and helps me take the next step. My alerts even calculate the money at stake for the return. That is the kind of AI system I want to keep building: not something that replaces my judgment, but removes the repetitive tasks that quietly consume my attention and that I’m most likely to defer.

I keep hearing some version of the same thing from my friends and family: “I already use ChatGPT and Claude, and I want to automate more of my work, I just don’t know where to start.”

If that sounds familiar, this guide will walk you through building your first scheduled workflow.

AI workflows can use several different capabilities, depending on what the work requires:

1. Answering: Responding to your questions in ChatGPT or Claude.

2. Working with files: Reading, analyzing, and creating documents, spreadsheets, and other files.

3. Retrieving information: Gather information from the web or other connected sources like email and Google Drive.

4. Taking actions: Creating or changing something in another system.

5. Running automatically: Starting work on a schedule or trigger, completing the workflow independently or pausing for your approval.

These are building blocks, not sequential steps. This guide focuses on how to identify the capabilities a workflow needs and set it to run on a schedule.

Step 1: Map your process

Before deciding how to automate something, map what happens today. Think through each step in detail:

1. What is the trigger for you to do this task? Is it time-based, such as every Friday afternoon, or event-based, such as receiving an email or uploading a document?

2. Where does the work happen? Which systems does it involve?

3. What are the inputs? What information do you need, and where does it live? Be specific: your inbox, a connected app, Google Drive, or a folder on your computer.

4. What are you doing with that information? What do you review, compare, categorize, decide, create, or change?

5. What are the outputs? What does the process produce, and where should that result appear or be sent?

6. Identify exceptions: What changes when information is missing, duplicated, ambiguous, or unusual?

7. Identify reviews: Where is your judgment required? What should AI prepare for you to review, vs. what is safe for it to complete without your approval?

This may seem like overthinking it, but these details determine which tools and type of automation you need. They also help you identify:

1. Where you spend the most time in the process.

2. Which steps depend most on your judgment.

3. Which repetitive, low-risk steps are the best places to start automating.

Here’s an example of a process map for someone handling Customer Support issues at a software company:

You don’t need to automate your entire process at once. Start with one tedious or time-consuming step that can produce value for you without creating much risk. Think about the tasks you find yourself doing repeatedly, either at work or in your personal life. If a task has a consistent set of steps, it may be a good candidate for automation. If you need inspiration, here are a few ideas:

1. Create a return-deadline tracker that searches order and return-confirmation emails and flags purchases approaching their return deadlines

2. Create a subscription monitor that finds upcoming renewals, trial expirations, annual charges, and announced price increases in email

3. Review your inbox and flag messages that appear to need a response

4. Turn meeting notes into tasks in a project management tool with owners and deadlines

5. Search project notes and messages for decisions that are still pending, along with owners and relevant context

Build your automation

Turn your process map into task instructions

Your automation needs instructions. Refer to your process map and describe what should happen in one complete run. At a minimum, specify the following:

1. The source(s): Which connected apps, accounts, files, or websites should the AI use?

2. The inputs: What information should the AI retrieve, and from what time period?

3. The work: What should it analyze, compare, categorize, create, or change?

4. The output: What should the result contain and where should it appear?

5. The exceptions: What should it do if information is missing, ambiguous, duplicated, or unchanged?

6. The review points: Which actions can it complete automatically, and which should pause for your approval?

You will add the task trigger later. For now, focus on describing what should happen each time the task runs.

Connect the systems your workflow needs

The AI needs access to the places where your information lives.

In ChatGPT, connected apps and reusable workflows are commonly installed through Plugins. Think of these like a toolbox for the app you’re connecting. For example, the Gmail plugin gives ChatGPT access to Gmail and may include reusable instructions for tools such as searching your inbox or drafting replies.

Open Plugins from the left navigation, find the application you need, click to install the plugin, and connect your account when prompted.

If you want to understand exactly what each plugin adds, you can open the plugin’s detail page and scroll to Skills. You can select any skill for more detail about the workflows it supports.

In Claude, the most direct way to connect an external application is through a Connector. Connectors give Claude tools for accessing and, when supported and authorized, taking actions in services such as Gmail, Google Drive, Slack, and Google Calendar.

Claude also offers Plugins, which can bundle connectors with skills and other workflow capabilities, but for the straightforward setup covered in this guide, start with Connectors.

Open Customize from the left navigation and select Connectors. Use the Add button to browse the Connector Directory, then Browse connectors to search for the application your workflow needs.

Open a connector to review its use cases and capabilities to see if it supports the information and actions your task requires. When you want to proceed, select Connect and follow the prompts to authorize your account.

Check what the connection can actually do

Just because ChatGPT or Claude can access a system doesn’t mean it can perform every action within it. Before building your workflow, confirm that the connection supports the specific information and actions your task requires.

If the application or capability you need isn’t available through a plugin or connector, the workflow may still be possible through a custom integration or automation platform. That is a more advanced setup I’ll address in a future article.

Determine whether the task needs local files or apps

You can manually upload an individual file through the ChatGPT or Claude apps in your web browser. However, if your task needs direct access to a folder, file, or application that only exists on your computer (and thus is not cloud based), then it must run locally through the ChatGPT or Claude Desktop apps. You will also need to grant them access to the relevant folder or application.

For a scheduled task that needs access to these local files or applications, your computer must also be powered on and awake, the ChatGPT or Claude Desktop app must be running, and the required files or applications must be available.

Is the workflow scheduled or event-triggered?

Look back at your process map. What triggers you to do this workflow? Does it begin at a predictable time, such as every Friday afternoon? If so, it can become a Scheduled Task directly in ChatGPT or Claude.

If your workflow should respond when something changes, such as an email arriving, a file being added to Google Drive, or a task status changing, a connected plugin may allow ChatGPT or Claude to check for that change on a schedule. However, the plugin does not necessarily notify the AI the moment the event occurs. If you need the workflow to begin immediately and the platform does not support that specific trigger, then you may need to leverage additional task-automation tools such as Zapier, n8n, or Make. This guide focuses on scheduled workflows, so I’ll cover those tools and event-driven workflows in a future article.

Test your workflow before creating the scheduled task

Before creating the scheduled task, run the workflow once in a ChatGPT or ChatGPT Work conversation, or in a Claude Cowork session. Provide the complete instructions and ask it to do one test run.

Confirm that it:

1. Accesses the correct sources and finds the intended information.

2. Performs the work and handles exceptions correctly.

3. Produces the output in a useful format.

4. Stops for your review where appropriate.

For workflows that send messages, modify records, or delete information, complete the first test in read-only or draft mode.

Revise your instructions as needed until the task completes as you would expect. Once it’s working, you can move on to scheduling the automation.

Schedule your automation

How to create a scheduled task in ChatGPT

1. You can create a scheduled task in ChatGPT in two ways:

   1. From an existing Chat or ChatGPT Work conversation: The AI will already have context if you’ve been discussing the workflow in a conversation. You can ask it to return to this same thread and share its output there each time it runs the task. The downside is the conversation can become very long over time, making previous results harder to review. You can read more about scheduled tasks inside a chat here.

   2. The Scheduled menu item in the left navigation: This creates a standalone task using self-contained instructions. Each run’s results will appear under Scheduled.

How to create a scheduled task in Claude

1. Select Scheduled in the left menu

2. Click New task

3. Choose Create with Claude if you want help translating your request into a task, or Set up manually if you already have your instructions written out.

4. If you choose Set up manually, enter the task name, instructions, choose an approval mode, select the project, default model, and schedule. You can read more about creating Claude scheduled tasks here.

Refine the task after its first few runs

Even if the workflow performs well during testing, expect to refine it after a few runs. This is especially important when the task involves subjective decisions such as determining what is important, relevant, urgent, or worth including.

Make sure to give specific examples in your feedback such as:

• If it leaves out useful information, define what it should prioritize, and why something was or wasn’t useful.

• If it retrieves too much information, add clearer filters or a threshold for inclusion.

• If the output isn’t organized the way you’d like, provide examples of a specific structure or template.

• If it makes too many assumptions, specify when it should flag uncertainty or request your review.

For instance, asking AI to “find the most important updates from the newsletters in my inbox” leaves significant room for interpretation. You may need to define examples of what is “important” to you and what should be excluded.

Scheduled tasks are only the beginning

One of the first systems I’m building this way is a recurring briefing from newsletters and other sources I already follow. I want it to do more than summarize. I want it to connect ideas across sources, surface counterpoints, and turn what I’m reading into an evolving personal knowledge base that helps me go deeper into the topics I want to learn more about.

I’ll break down how I’m designing that system in a future post, along with more practical guides to AI skills and event-triggered workflows.

In the meantime, I’d love to know: What’s a process you repeat at work or in life that you suspect should be easier by now? What have you tried to automate that succeeded or didn’t quite work the way you expected?

I felt like I’d accomplished something meaningful, until I looked at the clock at 11:30 p.m., a few nights before my trip, and sunk into my couch realizing I still didn’t actually have an itinerary. In my defense, I had hastily made one dinner reservation at Glug, naturally at an hour my friend warned me only American tourists would be eating.

To be fair, I enjoy the research. It gets me excited about a trip and helps me get familiar with a city’s vibe. But I also do it because I don’t want ChatGPT to generate the same itinerary every other tourist received, where we file in a single line through the same churches and eat at the same packed tapas bars in the Times Square of every city.

Some of that is unavoidable. I fully expect to see the major sites. But I also want something that feels more “me.” I want to see local neighborhoods, find quieter spots off the beaten path. So, I sift through TikTok and other social feeds, even though the same bakery appearing on my feed twelve times may be a sign that I’m not quite as far off the beaten path as I think.

I stared at my Google Maps chaos. I had hundreds of pins in Barcelona, with no practical way to distinguish the museums from the coffee shops, croissants, dinner spots, and everything else. I couldn’t easily tell which pins were on my “must-see” list. Then of course I had to consider the factor of proximity to El Born, the neighborhood of my Airbnb.

I was sure AI could help me distill this, but first I had to get the data out of Google Maps. Pasting in a Google Maps URL and connecting my Google account hadn’t given the AI access to my private saved places. Claude pointed me toward Google Takeout, Google’s free data-export tool.

Exporting all 3,500 saved places gave the AI two different kinds of context. My Barcelona pins became the pool of places it could organize into this trip, while my favorite places from other cities helped it understand what I tend to like: walkable neighborhoods, excellent coffee, local bookstores, and places with a little eccentricity. Instead of asking AI to invent a Barcelona itinerary from scratch, I was giving it years of evidence about my taste.

Once I uploaded the files, it took about 30 minutes to group my Barcelona saves geographically, flag logistically awkward attractions, prioritize my remaining reservations, and build a five-day itinerary around my existing plans. Just as importantly, I had a reusable travel taste profile that could make future trips easier to plan.

How to export your Google Maps saved places

Follow the step-by-step below:

1. Go to https://takeout.google.com/

2. Click “Deselect All”

3. Scroll down and select “Saved”. This includes your Google Maps lists, such as “Want to go,” “Favorites",” and any custom lists you’ve created. One exception: If you use Google Maps’ starred places instead of saving them to lists, also select“Maps (your places)” since those starred places live in a separate file and won’t show up in the “Saved” list export.

4. Scroll to the bottom of the list and click “Next Step.”

5. On the next screen, leave the default settings: An email download link, a one-time export, and a .zip file. Click “Create Export.”

6. Google says the export can take anywhere from a few minutes to a few days, but mine arrived within several minutes.

7. Check your inbox after a few minutes for the .zip file.

How to upload Google Maps saved places to ChatGPT or Claude

While you’re waiting for the export to arrive, create a dedicated Project in ChatGPT or Claude. A Project keeps your source files, relevant instructions, and planning conversations together.

Whenever you want to plan a new trip, you can just start a fresh chat in the same project instead of searching for an old chat conversation or re-explaining your basic preferences. In addition to the time savings, Projects are useful for a couple reasons:

• With your Maps data added as Project sources, ChatGPT or Claude can use those files as shared context in each new chat you start within that Project.

• This means you can start a separate chat for each trip instead of keeping one conversation going indefinitely. Your shared travel data remains available, while the dates, reservations, and decisions specific to each trip stay contained in a more focused thread.

Once the email arrives:

1. Download and unzip the export.

2. Open the subfolder titled “Saved.” Inside, you should see a separate CSV file for each of your lists, such as “Want to go” or “Favorites.”

3. Select the Maps-related CSV files and upload them to your new Project. In ChatGPT, open Sources, select “Add sources,” and upload the files. In Claude, select the “+” beside Files and choose “Upload from device.”

4. If you also exported “Maps (your places)” to include starred places, locate and upload that JSON file as well.

How to create an AI travel taste profile

If your export contained a “Favorites” list, it’s worth first asking the AI to analyze your preferences and create a “taste profile” to get a sense of how well it will curate its recommendations for you and give it any initial feedback.

I used a prompt along these lines:

Analyze my saved places and create a travel taste profile that can guide this trip and future recommendations. Treat my “Favorites” list as the strongest evidence of places I genuinely like; treat “Want to go” and other aspirational lists as signals of interest, not proof of preference.

Identify patterns in:

• The kinds of neighborhoods I gravitate toward

• Atmosphere, design, and level of formality

• Food, drink, shopping, and cultural interests

• My apparent tolerance for crowds and traditional tourist attractions

Support each conclusion with examples from my saves, distinguish strong patterns from tentative inferences, and flag any contradictory signals. End with a short list of attributes that make a place more likely to match my taste—and the types of places you would be more skeptical of recommending.

Reviewing this profile before itinerary planning gave me a chance to correct anything it had misread. It also showed me whether the AI was identifying meaningful patterns or simply producing a flattering but generic description.

Once the profile felt accurate, I asked it to use those preferences along with my Barcelona saves and known schedule constraints to begin planning the itinerary.

How to turn saved places into a personalized itinerary

You can use a version of the following prompt to generate your itinerary:

Using my uploaded Google Maps saves and travel taste profile, create a first-pass itinerary for [destination].

My trip details:

• Dates, arrival and departure times: [details]

• Where I’m staying: [address or neighborhood]

• Existing reservations or fixed plans: [details]

• Must-sees versus nice-to-haves: [details]

• Preferred pace and number of activities per day: [details]

• Walking tolerance and primary transportation: [details]

• Food, budget, weather, or other constraints: [details]

As you plan:

• Group places geographically and minimize unnecessary backtracking.

• Prioritize my saved places, but research additional options if there are important gaps.

• Check current opening hours, ticket availability, likely crowd patterns, and the weather forecast when relevant.

• Flag anything that needs to be reserved in advance.

• Identify any must-sees that are logistically inefficient or unrealistic to fit.

• Clearly label anything you couldn’t verify.

Before finalizing the itinerary, show me a rough structure, identify any conflicts or tradeoffs, and ask the follow-up questions you need answered.

Claude responded with a rough structure rather than jumping directly to a rigid hour-by-hour itinerary. It flagged places that were geographically awkward, asked whether I cared enough about them to justify the detour, and separated the reservations I needed to make immediately from places I could approach more spontaneously.

It also noticed details I hadn’t explicitly explained, like the lone running store among my saves, and asked whether I wanted to make time for a run during the trip.

I refined the draft through a few short follow-ups: which afternoon I wanted to leave open, which walking tour I preferred, and which sites I was willing to cut. This is where I wanted to spend my planning time, not evaluating every scheduling possibility, but making the tradeoffs that required my judgment.

From uploading the files to having a workable first draft took about 30 minutes. The itinerary still required my input and verification, but it replaced several hours of manually sorting pins, comparing neighborhoods, calculating the time to get between sites, checking booking requirements, and deciding what could realistically fit.

The surprising part was that the “Saved” export didn’t include separate geographic coordinates. It did include place names, addresses, and Google Maps links, which Claude could use, along with live research, to infer which places belonged in the same neighborhood. That was enough to produce a geographically sensible first draft rather than an itinerary that required me to cross the city repeatedly.

Why this works better than asking AI to plan from scratch

A generic AI-generated itinerary is only as personalized as the information you give it. I could spend 20 minutes explaining that I gravitate toward excellent coffee, quieter neighborhoods, independent shops, and restaurants that feel special without feeling overly formal. Or I could give it years of saved places that already demonstrate those preferences.

I still need to provide the constraints specific to this trip: My dates, walking tolerance, existing reservations, and how much unscheduled time I want. But I don’t have to translate years of taste into a collection of adjectives every time I plan somewhere new.

This doesn’t necessarily mean the AI uncovers “better” places than Google Maps, Tripadvisor, or a travel website. The improvement isn’t primarily in the discovery; it’s in the synthesis. It can turn hundreds of possibilities into a geographically coherent plan, identify what needs to be booked, and help me assess the tradeoffs when everything won’t fit.

It also changes where I spend my planning time. I used to sift through a spreadsheet, calculate transit times, determine how much could comfortably fit into a day, and research the best time to visit each attraction. Now I can focus on the decisions that actually require my judgment: whether I care enough about a particular site to cross the city or whether I’d rather prioritize a low-key dinner over squeezing in another museum.

Plus, since my saves and taste profile live in a dedicated travel Project, that context doesn’t disappear or get forgotten after Barcelona. I can refine it with feedback from my trip and use it as a better starting point for the next one.

Can you trust AI-generated travel recommendations?

It depends on what I’m trusting it to do. I trust AI to help organize my saves, identify patterns, surface logistical conflicts, and produce a strong first draft. I don’t treat it as the final authority on information that can change in the real world.

A saved-places export is only a snapshot. Restaurants close, opening hours shift, timed tickets sell out, and reservation policies change. Even when the AI conducts live research, it can misread an outdated page or present an inference with more confidence than it deserves.

Before finalizing the itinerary, I check official booking pages, current opening hours, reservations, ticket availability, and the final routes in Google Maps myself.

How to improve your taste profile over time

The itinerary saved me time once. The taste profile is the part that can become more useful after every trip, so I saved a concise version of it as a reference source in my travel Project.

I’m improving it over time by:

• Sharing brief feedback on what I loved, disliked, or found overrated and why. For example: “This day was theoretically walkable, but four scheduled stops left me exhausted that day.”

• Asking the AI to revise the taste profile based on that feedback, then replacing the old reference with the updated version.

• Uploading a fresh Google Maps export after a significant round of saving or before planning a trip for which I’ve added many new places.

I’d love to tell you I’ve stopped saving coffee shops in cities I’ve never visited at 11:30 p.m., but if anything, I’m saving more of them. The difference is that those saves now feel less like digital hoarding and more like actual inputs into my next trip.

Your past self already did the research. She just needed better tools to make it useful. The active setup is only a few minutes of checkboxes and uploads, while the payoff is finally being able to use years of accumulated saves.

Google Maps was the easy export. I haven’t yet solved what to do with all the Instagram saves, TikTok videos, and camera-roll screenshots I’ve accumulated, but this experiment is one example of the larger idea I’m building toward.

The Smart Girl Stack is where I test how AI can help us create more intelligent systems for everyday life so we can learn faster, work smarter, improve our finances, take better care of ourselves, and spend more time on the things we actually enjoy. Subscribe to see what works and what ultimately earns a place in my stack.

FAQ

Can you export your Google Maps saved lists?

Yes, through Google Takeout, Google’s free data-export tool.

Can ChatGPT access my Google Maps saved places directly?

Not through ChatGPT’s native Google connections. As of July 2026, connecting Google Drive or your Google account does not give ChatGPT access to your private Maps saved lists. A shared Maps URL may expose some public information, but it isn’t a reliable way to import an entire collection. The most reliable method I found is to export the lists through Google Takeout and upload the resulting files to a ChatGPT or Claude project.

Does the Google Maps export include coordinates?

Not in the “Saved” CSV files I exported. They contain place names, addresses, and Google Maps links, but no latitude and longitude fields. AI tools can often resolve those places and determine which neighborhoods they’re in using the names, addresses, and live research.

What’s the difference between the “Saved” export and “Maps (your places)”?

“Saved” exports your lists as CSV files, which is primarily what you want for this workflow. “Maps (your places)” exports a JSON file containing your starred places, which don’t appear in the “Saved” list export. If you tend to star places, export both “Saved” and “Maps (your places)”.

How do I organize hundreds of Google Maps saved places?

After exporting the data, upload the CSV files to a dedicated travel Project and ask the AI to analyze it. The goal isn’t to create a perfectly organized spreadsheet of all your saved places, it’s to make the relevant saves usable for this specific trip.

Do I need to upload a new export before every trip?

That’s up to you. Your existing files will still be there for the future. I’d upload a fresh export if you’ve saved a significant amount of new places for an upcoming trip, so that the AI has the latest.